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    Progress in the Development and Sharing of Big Data in Agricultural Science between China and Foreign Countries
    Zhao Ruixue, Zhao Hua, Zhu Liang
    Journal of Agricultural Big Data   2019, 1 (1): 24-36.  DOI:10.19788/j.issn.2096-6369.190103
    Abstract743)   HTML72)    PDF (1011KB)(1870)      

    Big data in agricultural science refers to the mass of scientific data that has accumulated and been integrated over a long period in scientific and technological activities associated with agriculture. These data not only directly reflect the overall basic level of agricultural science and technology in a country, but also affect the sustained and stable development and improvement of agricultural science and technology in the long term; they are valuable for resource preservation, development and use. As part of data-intensive scientific research, big data in agriculture are basic strategic resources that support the development of innovations in agricultural science and technology, and the development of modern agriculture, which are related to the country's strategic interests and security. To promote sharing and use of scientific big data in agriculture, literature reviews, websites survey and comparative analysis have been conducted. This review summarizes the development strategies and sharing policy of scientific big data , and analyzes the progress of the development and sharing of big data in agriculture. Focusing on the future development of big data in agriculture and providing a reference for the development and sharing of such data in China, this review provides suggestions for policy formulation and implementation, data development models and resource integration, data opening and publication, and other issues.

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    The Development Direction of Fresh Agricultural Products Supply Chain in China after COVID-19
    Dongnan Li, Guoyang Pan, Qian Zhou, Bin Li
    Journal of Agricultural Big Data   2020, 2 (3): 42-51.  DOI:10.19788/j.issn.2096-6369.200305
    Abstract1430)   HTML35)    PDF (687KB)(1554)      

    Fresh agricultural products mainly include vegetables, fruits, meat, eggs, milk, and aquatic products. The degree of freshness of perishable primary products determines their own value. In 2020, the COVID-19 outbreak has had a huge impact on people's lives, further affecting the development process of China's fresh agricultural products industry and posing new challenges to the existing supply chain system of fresh agricultural products. Our investigation and analysis show that during the epidemic period, there were negative phenomena such as the contradiction between supply and demand of fresh agricultural products, the slow information transmission of the supply chain, and the narrow purchase channels of suppliers. At the same time, we also identify positive outcomes, such as the recovery of cold chain logistics and new developments in the fresh agricultural supply chain. To further understand the outbreak period, we conducted a survey on the running status of fresh agricultural products. This paper investigates the rise in purchases of fruits and vegetables by studying data from 500 consumers in seven regions including central and southern region. We also investigate the problem of the quality of fruits and vegetables and the future development of the fruit and vegetable industry, and we identify three ways of understanding the changes in the fruit and vegetable supply system since the purchasing outbreak. The study concludes that during the imbalance in the supply and demand of fruits and vegetables, when demand for fruits and vegetables is high, problems arise because of limitations of the fresh product supply system in China. Four factors are important to the future development of the fresh agricultural products supply chain system: the integration of the online supply chain model; the wisdom of the logistics supply chain; the strategic alliance model of agricultural products supply chain; and the demand of the agricultural products supply chain pattern. Together these four factors can ensure the stability and orderly development of the fresh agricultural products industry in the context of normalized epidemic prevention.

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    Research and Application of Big Data in Agriculture
    Jiang Hou, Yang Yaping, Sun Jiulin
    Journal of Agricultural Big Data   2019, 1 (1): 5-10.  DOI:10.19788/j.issn.2096-6369.190101
    Abstract1226)   HTML152)    PDF (2372KB)(1414)      

    Against the background of China's strategy of promoting rural revitalization, big data has become a hot topic in agricultural research and application. Building on the basic characteristics of big data, this paper introduces the concept of agricultural big data and its typical characteristics. It summarizes current approaches for collecting agricultural big data, describes the application system and key technologies of an agricultural big data platform, and discusses the use of agricultural big data for agricultural decision-making, intelligent production, market match, agrometeorology and food safety. Finally, the paper examines the current difficulties in using agricultural big data. In sum, the paper contributes a foundation of understanding for the innovation and development of agricultural big data.

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    Progress in the Application of Big Data in Agriculture in China
    Zhou Guomin
    Journal of Agricultural Big Data   2019, 1 (1): 16-23.  DOI:10.19788/j.issn.2096-6369.190102
    Abstract1099)   HTML138)    PDF (915KB)(1353)      

    Big data have become a new resource in modern agriculture and an important focus for technological innovation in agricultural science. Big data not only promote the production, operation, management and service provisions of modern agriculture, but also advance the integration of primary, secondary and tertiary industries. In developed regions such as Europe and the United States, special attention has been paid to the role of big data in modern agriculture; in China, research and application of big data in agriculture have also developed rapidly. Agricultural data generally have large spatial and temporal coverages, and are difficult to collect and complicated to process. Therefore, it is of great significance for the research and application of agricultural big data in China to systematically review the progress regarding the application of such data and further clarify the direction of future development. In this study, a literature review is combined with related scientific research; definitions of big data in agriculture proposed by different researchers are compared and analyzed; concepts and matters relating to big data in agriculture are explained; and progress in the application of big data in agriculture in management and policy, engineering and application, and technology and infrastructure in recent years is systematically summarized. Finally, based on the current situation regarding the development of big data in agriculture in China, this review suggests three aspects where special attention should be paid to promote its development: the issues of platform and data, demand and application, and trade and sharing.

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    Big Data of Plant Phenomics and Its Research Progress
    Chunjiang Zhao
    Journal of Agricultural Big Data   2019, 1 (2): 5-14.  DOI:10.19788/j.issn.2096-6369.190201
    Abstract1705)   HTML143)    PDF (1041KB)(1227)      

    Plant phenomics is capable of acquiring gigantic multi-dimensional, multi-environment, and multi-source heterogeneous plant phenotyping datasets through integrated automation platforms and information retrieval technologies, based on which the big-data driven plant phenomics research is established. This emerging research domain aims to systematically and thoroughly explore the internal relationship between "gene-phenotype-environment" at the omics level, so that phenomics methods can be utilized to unravel the formation mechanism of specific biological traits in a comprehensive manner. As a result, it is greatly catalyzing the research progress of functional genomics, crop molecular breeding, and efficient cultivation. In this paper, we summarized the background, definition, initiation, and features of the big-data driven plant phenomics, followed by a systemic overview of the progress of this field, including the acquisition and analysis of plant phenotyping data, data management and relevant database construction techniques for administering big data generated, the prediction of phenotypic traits, and its connection with the plant omics research. Furthermore, this paper focuses on discussing present problems and challenges encountered by both plant research and related applications, including (1) the standardization of collecting plant phenotypes, (2) research and development (R&D) of diverse phenotyping devices, supporting facilities, and low-cost phenotyping equipment, (3) the establishment of big data platforms that can openly share phenotyping data and phenotypic traits information, (4) theoretical approaches for fusion algorithms and data mining techniques, and (5) collaborative, sharing and interactive mechanisms for the plant phenomics community to adopt. Finally, the paper puts forward suggestions in four aspects that need to be strengthened: (1) systematic design and standards of plant phenomics research, (2) revealing the mechanism of plant phenotype and environtype to facilitate intelligent equipment R&D, (3) the establishment of big data for plant phenomics, and (4) the formation of collaborations through academic networks and specialized research groups and laboratories.

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    A Framework for Agricultural Big Data Standards
    Yanmin Yao, Yuqi Bai
    Journal of Agricultural Big Data   2019, 1 (4): 76-85.  DOI:10.19788/j.issn.2096-6369.190408
    Abstract725)   HTML29)    PDF (622KB)(1195)      

    As agricultural production, management, operations, and services enter the big data era, standardization is becoming increasingly important in ensuring the comparability between the collected results generated by diverse devices and instruments, compatibility between multi-source data, integrability between multiple types of data analysis systems, the quality of agricultural products at a global scale, and the coherence between different production and management processes. This article summarizes the domestic and foreign standards in the field of agricultural big data. There are currently few standards and norms that can directly guide the development of agricultural big data. Research studies for a big data standards framework are critically needed to guarantee and promote the continuous and in-depth development of agricultural big data applications. This paper draws on the methods proposed by the International Organization for Standardization and International Electrotechnical Commission to analyze the standard system for a framework from the perspectives of enterprice, information, computation, engineering, and technology. From informational and computational perspectives, the needs in developing agricultural big data standards were analyzed, and a framework for an agricultural big data standard system is proposed. This framework contains standards for fundamental guidelines, general practice, and applications. Among these needs, the fundamental guidelines for agricultural big data are the basis for the formulation and coordination of agricultural big data standards, including national big data-related laws, regulations, policies, and national standards related to big data. The general standards for agricultural big data include four categories of common agricultural standards: i.e., agricultural big data foundation, agricultural big data collection and processing, agricultural big data management, and agricultural big data-sharing services. The agricultural big data application standards are the agriculture standards and regulations formulated for specific parts of the whole process of agricultural big data, such as agricultural elements and ownership information, agricultural production processes, and agricultural operation and management. The design of the agricultural big data standard system is a complex and huge systematic project that requires the participation of multisector and multidisciplinary personnel, and it is one of the top priorities in agricultural big data development.

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    Research and Design of Agricultural and Rural Digital Resources Architecture
    Dong Tang Wenfeng Du Weicheng Sun Guangrong Jia Xinwei Liang
    Journal of Agricultural Big Data   2019, 1 (3): 28-37.  DOI:10.19788/j.issn.2096-6369.190303
    Abstract795)   HTML32)    PDF (586KB)(1114)      

    The digital economy is a new form of economic and social development that arose after the agricultural economy and industrial economy, and it is one of the important engines leading global economic growth. The digital economy has become the most active area of China's economic development and an effective way to achieve sustainable economic development. Agriculture is the basic industry in China, and digital agriculture is an important part of China's digital economy. The development of digital agriculture is conducive to the realization of accurate decision-making and guidance in agricultural production, the promotion of agricultural competitiveness, and the sharing of digital economic development dividends among all farmers. The key factor of production in the digital economy is digital knowledge and information, namely digital resources. Digital resources in agricultural and rural areas include information such as databases, electronic documents, pictures, videos, web pages, and remote sensing images. At present, China's agricultural and rural informatization level is not high, and digital resources are relatively scarce. There are some practical problems in agricultural and rural digital resources, such as decentralization, inconsistent standards, and a lack of a digital resource management system covering the whole life cycle of data. It is necessary to carry out research and design work on the architecture of agricultural and rural digital resources, establish relevant standards and norms from a top-level perspective, improve and enhance the organizational form of digital resources, and build a blueprint for agricultural and rural digital resources. This paper analyses the classification of agricultural and rural digital resources by the Food and Agriculture Organization of the United Nations(FAO) United States Department of Agriculture(USDA) and designs the whole life cycle of digital resources covering agriculture and the countryside. This life cycle includes planning, design, collection, storage, processing, management, service, and use of data. Further, this paper establishes the architecture of digital resources in agriculture and the countryside. It focuses on the structure of an agricultural and rural thematic database, and presents the key construction tasks and development suggestions for improving the agricultural and rural digital resources system.

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    Accurate Precipitation Nowcasting with Meteorological Big Data: Machine Learning Method and Application
    Zhang Chenyang, Yang Xuebing, Zhang Wenshen
    Journal of Agricultural Big Data   2019, 1 (1): 78-87.  DOI:10.19788/j.issn.2096-6369.190108
    Abstract800)   HTML26)    PDF (5172KB)(1075)      

    Accurate precipitation nowcasting is essential for agricultural production, hydrological monitoring, flood mitigation, large organizations, and electrical systems. Because of the high uncertainty of weather systems, the performance of precipitation estimation by conventional meteorological methods based on physical models and statistical analysis is unsatisfactory. Determining how to improve the accuracy of precipitation estimation and forecasting in high resolution is challenging. This study proposes the method of terrain-based weighted random forests (TWRF) for radar-based quantitative precipitation estimation (QPE). This method can be regarded as a generalization of random forests via consideration of variations in the vertical profile of reflectivity (VPR) and orographic enhancement of precipitation for complex terrains. The performance was tested within the 45~100 km range of the Z9571 radar in Hangzhou, China during rainfall events in June and July, 2014. The experimental results showed that TWRF is better than conventional methods and random forests, and further indicate that utilization of the entire VPR and terrain-based modeling are effective for radar QPE.

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    Construction of an Agricultural Big Data Platform for XPCC Cotton Production
    Xin Lv, Bin Liang, Lifu Zhang, Fuyu Ma, Haijiang Wang, Yangchun Liu, Pan Gao, Zhangze, HouTongyu
    Journal of Agricultural Big Data   2020, 2 (1): 70-78.  DOI:10.19788/j.issn.2096-6369.200109
    Abstract1278)   HTML51)    PDF (1192KB)(1014)      

    The Xinjiang Production and Construction Corps (XPCC) have created a modern cotton planting system with regional characteristics in China. This new system advances Chinese production techniques in the aspects of agricultural intensification, scale, development of agricultural machinery, and application of modern agricultural technology. During the years of systematic development and performance, massive data were accumulated by XPCC in the cotton planting field. As big data technology has become an important driving force for the development of intelligent agriculture in China, how to apply this technology to further improve the intelligent level of the cotton planting system and realize the healthy, efficient, and sustainable development of the whole cotton industry chain is a key problem in strengthening and enhancing XPCC’s ability to reclaim and defend the Chinese border in the information age. Thus, we constructed a big data platform that covers the entire industrial chain for cotton production in China based on a mature commercial big data storage and analysis system framework to promote the cooperation of industry, colleges, and institutes for cotton production big data in the XPCC. This platform was comprised of data, model, system, and application layers from the bottom up. In each layer, the cotton production chain was analyzed using five dimensions of agricultural resources, agricultural monitoring, production management, agricultural machinery scheduling, and market prediction. After completion, the developed platform intends to provide big data for comprehensive management and sharing, remote-sensing monitoring, agricultural machinery operation monitoring and maintenance, intelligent decision-making, quality traceability, and market early warning and prediction services for cotton production to potential users involved in cotton production and management. Finally, this paper analyzes the problems encountered in data sharing, model upgrades, and service modes in the process of platform research and development, and puts forward some suggestions to provide references for agricultural big data resource sharing and platform construction in China.

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    A Survey of Big Data Deep Learning Systems and a Typical Agricultural Application
    Lingxu Zhang,Rui Han,Wenming Li,Yinxue Shi,Chi Liu
    Journal of Agricultural Big Data   2019, 1 (2): 88-104.  DOI:10.19788/j.issn.2096-6369.190208
    Abstract861)   HTML40)    PDF (2358KB)(952)      

    With the rapid development of information age, big data has become the key technology to promote people's production and daily life to undergo major changes, and plays a very important part in the development of various fields, including agriculture. In order to effectively analyze and utilize the big data and make it play its maximum value, the research and development of deep learning technology plays a decisive role. In this context, this paper gives a detailed introduction to the main technical characteristics and development of big data deep learning system, including deep learning model (such as CNN model and RNN model), optimization algorithm, big data learning framework, hardware configuration and so on. This paper also explains the technical characteristics and development process of five mainstream deep learning frameworks, including PyTorch, and compares the strengths and weaknesses of these frameworks. In addition, this paper also mentions the typical application of big data deep learning system in agriculture, "Grape Leaf Downy Mildew Forecasting System Based on Big Data", and takes its key step "Grape Leaf Classification and Recognition Process" as an example to introduce its working principle in detail, including data collection, sample feature extraction, clustering algorithms, classification algorithms and result analysis. This system uses big data and deep learning technology to help detect and prevent downy mildew of grape leaves. Finally, this paper introduces the main development trend of big data deep learning system, as well as the problems requiring attention in agricultural research and application. Today, big data deep learning system is playing an increasingly important role and has been widely used in the field of agricultural data analysis, including crop pest prediction.

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    Design of an Agricultural Product Supply Chain Management System using Blockchain Technology
    Chenxue Yang, Zhiguo Sun
    Journal of Agricultural Big Data   2020, 2 (2): 74-83.  DOI:10.19788/j.issn.2096-6369.200208
    Abstract836)   HTML38)    PDF (1013KB)(826)      

    Agriculture is the main industry in China. A safe, credible, stable, traceable, information-sharing, and large-throughput agricultural supply chain system is needed to achieve agricultural informatization. At present, information about China’s agricultural product supply chain has been stored in a centralized database and file system, with weak information management capabilities, leading to problems such as theft, tampering, deletion, and inconsistencies. In light of these problems, an efficient extraction method which combines an interstellar distributed file system and smart contract technology for heterogeneous information data in agricultural product supply chain is established. This method achieved fast extraction of large heterogeneous information transaction logs which including agricultural product production information, recording transportation information, consumer credit information, farmer-consumer transaction services. All kinds of information transaction logs and transaction records are stored in blockchain and IPFs network respectively. It proposed an access control method for agricultural product supply chain information data based on blockchain smart contracts, combined with an access method based on IPFS hash addressing, which can effectively ensure different agricultural production and operation participants access data within their authorities. A data management system of agricultural products supply chain based on blockchain technology is designed, which can ensure the transforming and upgrading of agriculture, thereby helping farmers increase their income and eliminate poverty.

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    Preliminary Exploration of Big Data Security Supported by Blockchain in Agriculture
    Zhongfu Sun, Juncheng Ma, Feixiang Zheng, Keming Du
    Journal of Agricultural Big Data   2020, 2 (2): 25-37.  DOI:10.19788/j.issn.2096-6369.200203
    Abstract414)   HTML15)    PDF (1259KB)(688)      

    Big data constitutes a core technology for smart agriculture. However, there are a number of issues related to security with big data that could limit progress with smart agriculture. Following the speedy development of blockchain technology and in light of its intrinsic security characteristics. Big data could provide a new driving force and means for big data to be safely applied. This paper addresses some challenges related to supporting the safe development of big data. Briefly described the current status of China's agriculture and the background of the development of smart agriculture. Regarding basic issues concerning smart agriculture, a systematic study is made of the mutual relationship between agricultural big data security and blockchain technology which involves a brief introduction to the significance of big data, existing risks of big data, current challenges, and measures to promote bigdata safety. Comprehensive analyzed the security characteristics of the blockchain, described the supporting status and roles of blockchain in big data security, and explained three core directions of blockchain data security, which are data confidentiality, integrality, and availability. In conclusion, proposed suggestions and future applications on how to use blockchain to help the security of big data development.

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    Big Data Applications and Construction for the Shandong Potato Industry
    Jia Ruan Huaijun Feng Wenjie Zhao
    Journal of Agricultural Big Data   2020, 2 (1): 29-35.  DOI:10.19788/j.issn.2096-6369.200104
    Abstract421)   HTML7)    PDF (727KB)(655)      

    This paper analyzes the existing data for the Shandong potato industry to understand its production, circulation, storage, and processing characteristics, among other elements. According to the data analysis of production in the Shandong potato industry, potatoes are planted in spring and autumn, with Tengzhou and Jiaozhou as the main planting areas. According to the data analysis of circulation, the market price fluctuation shows the rule of “three years/one cycle,” with outstanding and refined management benefits. Potato exports from Shandong are the highest among potato exports in China. According to the data analysis of storage, the potato storage capacity can still meet local needs and cold storage constructions have been developed to include more modern and constant temperature storage facilities. According to the data analysis of processing, improvement is still needed in processing capacity. This paper outlines the construction of a big data platform for a single variety of Shandong potato. Modern precision agriculture in production will develop a measurement system that integrates all production elements and spaces will be constructed to guide intelligent production. In the circulation element, online e-commerce trading and safety traceability systems for potatoes will be constructed in addition to developing the quality of agricultural products. For the storage element, a potato refrigeration–packaging industry will be promoted, including the accelerated establishment of cold storage, warehouse storage, and logistics alliances. In the processing element, a staple potato food processing industry will be developed alongside the promotion of the cluster development of processing enterprises. We will effectively strengthen top-level designs, consolidate and integrate research into key technologies for big data, and promote data sharing, opening up, development, and utilization. By speeding up the adjustment of regional layouts, preventing excessive fluctuation of potato prices, and strengthening the information analysis and early warning for the whole industry chain, we can better lead the sustainable and high-quality development of the Shandong potato industry.

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    Development and Visions of Blockchain Technology
    Hui Li, Yuming Yuan, Wenqi Zhao
    Journal of Agricultural Big Data   2020, 2 (2): 5-13.  DOI:10.19788/j.issn.2096-6369.200201
    Abstract951)   HTML63)    PDF (1126KB)(622)      

    Blockchain (or distributed ledger) technology was introduced in 2008, when the famous Bitcoin cryptocurrency was initiated. Blockchain has been undergoing rapid growth in both academia and industry. Today, it is no exaggeration to say that blockchain has become a new, independent research topic—not a subtopic subsumed within cryptocurrencies. From a technical perspective, blockchain technology is based on various fundamental computing technologies, such as advanced cryptography, distributed data storage, peer-to-peer networking, and distributed consensus protocols. Generally, blockchain technology involves creating a shared, distributed ledger: that ledger can offer great flexibility and potential in resolving many important challenges in a complex computing context that involves multiple parties. Examples of such challenges include achieving mutual trust, privacy protection, and data consistency in large-scale business scenarios. Many business applications have already covered a broad range of industrial services, such as those related to finance, governance, medicine, and city construction. Blockchain technology is becoming increasingly adopted and applied; however, the current design of blockchain is practically far from sufficient—especially when dealing with critical domain challenges. Specifically, the key limitations of blockchain mainly derive from poor system scalability, weak resilience to external security attacks, and lack of computing interfaces for regulatory processes. Conversely, it is the very shortcomings of blockchain technology that motivate research efforts into many related technologies. Based on conventional blockchain design, new functional extensions and cryptography technical optimizations have been continuously proposed by researchers and practitioners: the aim is to make blockchain technology more practically applicable and meet various demands of different business users. In this overview paper, using the latest findings from both academic and industrial research, we systematically present the general architecture of blockchain technology with its five functional layers. The five-layered architecture comprises the following: a data layer; a network layer; a distributed consensus layer; a smart contract layer; and an application layer. We also provide a technical description of key theories and important techniques related to each functional layer. From the proposed general architecture of blockchain technology, we offer an in-depth explanation of its core technical extensions with respect to the following: blockchain integration with existing computer network techniques; the blockchain framework itself and important modules; and underlying critical cryptography techniques. Further, we discuss potential contributions that these promising technical extensions could provide with respect to reshaping and optimizing blockchain technology. Finally, following current developments with blockchain technology and its existing mainstream applications, the general views about future challenges and important directions for this technology is to facilitate future follow-up research.

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    Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat
    Guohui Ding,Hao Xu,Mingxing Wen,Jiawei Chen,Xiue Wang,Ji Zhou
    Journal of Agricultural Big Data   2019, 1 (2): 19-31.  DOI:10.19788/j.issn.2096-6369.190202
    Abstract877)   HTML119)    PDF (5195KB)(577)      

    [Multi-scale plant phenotyping technologies are capable of collecting big vision-based and spectroscopic datasets, based on which reliable phenotypic analysis can be carried out through a range of computational algorithms based on computer vision and machine learning techniques. Quantitative traits measurement is key to crop genetics, breeding, cultivation and agricultural practices, because they can be used to dynamically evaluate yield, quality and stress resistance in a high-throughput and reproducible manner. As an important staple crop in China, it is essential to establish a systematic approach to monitor wheat growth and quantify yield-related traits during key growth stages. In this work, we firstly reviewed important yield-related traits for bread wheat and then developed a field phenotyping approach to collect a number of common traits using cost-effective and low-altitude unmanned aerial vehicles (UAVs). Based on the visible spectrum images acquired in a field experiment, we utilized professional software (i.e. Pix4Dmapper) to stitch UAV sub-images as well as to reconstruct 3D point cloud to represent the whole experimental field. After this phase, we developed an automated traits analysis pipeline to produce the vegetation map (e.g. Excess-Green index, ExG) and measure important yield-related traits. We have quantified plant height, vegetation index (e.g. ExG) and leaf area index at five key growth stages for 18 wheat genotypes. Our work validates that yield-related traits can be acquired through cost-effective UAVs, which can lower the threshold of conducting field phenotyping and reliable phenotypic analysis. Our work also exhibits a promising approach for research groups and organizations to follow standardize data collection, phenotyping data ontology, as well as the utilization of open-source analytic libraries to develop high-throughput phenotypic analysis techniques in crop phenotyping research.

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    Spectral and Imaging Datasets of Apple Leaf Disease and Insect Pests in China in 2015
    Fei Gao, Xiaoli Wang, Tingting Liu, Zhuang Li, Rui Man
    Journal of Agricultural Big Data   2020, 2 (4): 120-124.  DOI:10.19788/j.issn.2096-6369.200415
    Abstract759)   HTML44)    PDF (1305KB)(559)      

    China’s apple planting area and total output rank first in the world, but most of the current collection of spectral and imaging data from fruit trees focuses on apple leaf diseases and insect pests. In this study, the spectral reflectance and imaging data from apple leaves having three different diseases, spot leaf fall and powdery mildew, or the red spider insect pest, were collected from the National Apple Resource Nursery to provide data for the effective identification of apple leaves exposed to different diseases and insect pests. This will provide a foundation for the future use of aerospace remote sensing to monitor large-scale fruit tree diseases and associated insect pests.

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    The Status and Trends of Scientific Data Sharing Systems
    Yunting Li, Liangming Wen, Lili Zhang, Jianhui Li
    Journal of Agricultural Big Data   2019, 1 (4): 86-97.  DOI:10.19788/j.issn.2096-6369.190409
    Abstract481)   HTML18)    PDF (800KB)(501)      

    Data-intensive research is emerging as a new paradigm for science discovery in the era of big data, and the use of open data has become common in the scientific community. Over time, different models of scientific data sharing have emerged, including scientific instruments models, data platforms models, data publishing models, crowdsourcing and data market models. Correspondingly, a variety of solutions have emerged for different fields and applications, such as data repositories, data federated services systems, data distribution systems, and on-demand computing and analysis cloud services systems. This paper examines development and future trends in scientific data sharing systems, using the Big Earth Data Cloud Services Platform as an example. It analyzes and compares the typical services and technical characteristics, using scenarios and representative systems of the above-mentioned four types of mainstream scientific data sharing systems. Our analysis suggests that future scientific data sharing systems will focus on the need to manage the full life-cycle of scientific data and will converge into a cloud service system providing functions such as data acquisition, storage, distribution and sharing, analysis, and intelligent services. By making data FAIR (Findable, Accessible, Interoperable and Reusable), machine understandable and AI-Ready, promote the formation of data sharing eco-systems.

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    From Liangping Pomelos to Meizhou Pomelos: Using Blockchain Technology to Trace the Origins of Agricultural Products
    Linli Jiang, Lei Shi
    Journal of Agricultural Big Data   2020, 2 (2): 94-103.  DOI:10.19788/j.issn.2096-6369.200210
    Abstract379)   HTML13)    PDF (1367KB)(478)      

    Traceability of agricultural products involves tracking such products in a positive, reverse, or non-directional manner. That traceability covers all processes: from planting and production, logistics and transport, warehousing and sales through to consumer terminals; it does so by means of special information collection equipment, intelligent information-control systems, and information sharing. In that way, consumers are able to obtain the information they need. Given the occurrence of increasing numbers of food safety incidents and improvement of people’s material living standards over recent years in China, citizens’ demands for the quality and safety of agricultural products have grown. The traditional traceability system of agricultural products has exposed ever-greater problems. However, meeting consumer needs has also become increasingly difficult. Thus, it is necessary to establish a set of transparent guidelines in this regard. There is an urgent need in China to establish a credible traceability system for agricultural products—a system that can better guarantee the quality and safety of such products. Blockchain technology is essentially a shared database: stored information is characterized by non-forgery, non-tampering, full traceability, openness, and transparency. By combining blockchain technology with the Internet of Things, artificial intelligence, time stamp encryption, and other technologies, it is possible to establish a traceability system for agricultural products to cover planting, picking, circulation, storage, sales, and production. By focusing on Liangping pomelos and taking Meizhou pomelosas an example, this study analyzes the application of the conventional traceability system for Liangping pomelos; it exposes the problems associated with applying that system. This paper also examines the application of blockchain technology for the traceability of grapefruit. The framework of such a system is presented. A discussion is made of the traceability mechanism with respect to obtaining planting information, cold chain transportation, supervision and quality inspection links, and consumption links. Finally, this paper summarizes the application of blockchain technology in the traceability of other agricultural products; it identifies current problems, thereby setting the stage for further innovation and development.

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    Developing a Combined Map of the Spatial Distribution of Autumn Crops in Henan Province Using Multi-source Remote Sensing
    Laigang Guo Yan Wang Lijun He Jia Yang Xiuzhong Zhang Hongli Zheng Guoqing Wang
    Journal of Agricultural Big Data   2020, 2 (1): 53-59.  DOI:10.19788/j.issn.2096-6369.200107
    Abstract726)   HTML26)    PDF (1037KB)(477)      

    Mapping the spatial distribution of all crops, using remote sensing, is important for monitoring agricultural production in China. The classification results provide basic data for quantitative and scientific management.


    The spectral characteristics of different autumn crops are known from years of experience using remote sensing to monitor crop planting areas. A combined map of crop spatial distribution in Henan Province in 2019 was made using multi-source remote sensing and different classification methods, based on topographic and geomorphic characteristics and crop planting structures. The classification crops include corn, peanuts, rice and soybeans, and the multi-source images include Sentinel-2, Landsat 8-OLI, GF6 WFV with different spatial resolutions. The accuracy of the classification results was verified using ground samples and survey data.


    The planting structure of autumn crops is complicated, including single crops of corn and rice, as well as mixed crops of corn, peanuts and soybeans. In the study area, corn was planted over the largest area, followed by peanuts. The overall accuracy and Kappa coefficient of the combined map were 86.13% and 0.83, respectively, which met the basic requirements for monitoring crops at provincial scale.


    The paper addresses the optimization of remote sensing data sources, classification methods, ground surveys and precision verification approaches. In this study, the technical ability to monitor crops using remote sensing of planting structures was tested in a large-scale business application. It offers technical support for using remote sensing to produce a combined map at large scale.

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    The development of deep learning based Natural Language Processing (NLP) technology and applications in agriculture
    Cui Yunpeng, Wang Jian, Liu Juan
    Journal of Agricultural Big Data   2019, 1 (1): 38-44.  DOI:10.19788/j.issn.2096-6369.190104
    Abstract1301)   HTML72)    PDF (10801KB)(468)      

    Deep learning is an emerging but rapidly advancing technology having a profound impact on modern natural language processing (NLP) technology. This paper discusses recent developments of NLP technology driven by deep neural networks (DNN), as well as new products and recent cases. In particular, the paper examines advances relevant to the agriculture domain, such as DNN-based word embedding vector construction, the computational ability to recognize and name domain-specific entities and agricultural literature terms. Additionally, it analyzes the implementation details of related technologies. Finally, the paper reviews the trends and outlook for NLP technology, highlighting the significance of NLP technology for intelligent applications in agriculture.

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    Research on Construction of Fisheries Science Data Center
    Feng Lu,Lihua Wang,Shuo Xu
    Journal of Agricultural Big Data   2019, 1 (3): 57-70.  DOI:10.19788/j.issn.2096-6369.190306
    Abstract473)   HTML32)    PDF (1779KB)(441)      

    Basic, original fisheries science data is generated in the process of fishery technological activities, and has important scientific significance and practical value for agricultural, marine and economic fields. The Fishery Science Data Center, which manages fishery science data and applications of that data, is a crucial strategic resource for technological innovation and industrial development. It also provides important technical support to development strategy and scientific decisions, and improves the modernization of fishery. This study analyzes the characteristics, sources, and possible applications of fishery science data in the context of needing for fishery science data application, with the goal of improving the comprehensive service and smart decision-making ability of the data center and effectively preserving, managing, sharing and mining fishery science data.In the context of needing for fishery science data application We analyze and describe the function and position of the data center in terms of the demands for scientific data in the fishery technological innovation process. The overall architecture of the data center supports data fusion, big data analysis and cloud computing services. Technical roadmaps identify a storage and fusion platform for multi-source heterogeneous fishery data, a big data analysis and application platform in fishery science, and a cloud service platform. The study also considers factors in the sustainable development of the data center, including data collection, systems construction, standards setting, shared services mode, skills and training, and energy saving. The end goal is to ensure the continuous operation of the data center, maximize the value of fishery science data, point the direction for further building fishery science data .

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    Prospect of Agricultural and Rural Application of Trusted Blockchain
    Wensheng Wang
    Journal of Agricultural Big Data   2020, 2 (2): 14-24.  DOI:10.19788/j.issn.2096-6369.200202
    Abstract482)   HTML21)    PDF (1176KB)(408)      

    The potential of blockchain applications for promoting the high-quality development of agriculture and rural areas has received attention worldwide. China is currently in a mature stage of national poverty alleviation and a crucial period for rural revitalization. The weakly-centralized/ decentralized nature of Chinese agricultural and rural industrial and social structures suggest a natural coupling and application integration point with emerging blockchain technology. However, the majority of agricultural and rural blockchain research efforts focus mainly on rural finance and approach issues in a fragmented manner. In contrast, explores the application of blockchain technology in agriculture and rural areas from the holistic perspective of the national "comprehensive rural revitalization" strategy. After reviewing the technical characteristics of blockchain and related global application research, we systematically examine prospects in agricultural and rural development, and identify blockchain opportunities in eleven domains, such as smart agriculture based on trusted Internet of ThingsIoT , credible support networks for the new agricultural management systems, agricultural product quality and safety systems, credible rural collective asset management system, credible rural tourism industry, rural living environment improvement initiative, targeted poverty alleviation, credible migrant workers' rights and interests protection network and so forth. Our findings suggest that the combination of blockchain and 5G, artificial intelligence and other "new infrastructure" elements can accelerate the evolution of society to society 5.0.

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    Construction and Application Prospects of Big Data Platform for Plant Protection in Anhui Province
    Meng Dong Wei Qian Rong Yang Qianjin Zhang Liping Zhang
    Journal of Agricultural Big Data   2020, 2 (1): 36-44.  DOI:10.19788/j.issn.2096-6369.200105
    Abstract443)   HTML21)    PDF (910KB)(407)      

    In recent years, China’s agricultural plant protection has become increasingly important, and the forecasting system of agricultural plant protection is not perfect. The basic reason is that the collection, mining, and decision-making ability of agricultural big data are insufficient. At present, there are many information platforms related to agricultural plant protection on the market, but they all face problems, such as insufficient classification of resources and inaccurate resource information. As a major agricultural province, the problem of plant protection in Anhui Province is particularly serious. To promote the development of a plant protection system in Anhui Province, this article uses the digital image library of agricultural plant protection as the bottom layer, and aims to realize the distributed storage and processing of data resources by constructing a platform for big data management, analysis, mining, and visual display. To meet the actual requirements of agricultural production, management decision making, and technological innovation, we conduct technical research and product research on a system framework, data cleaning, data mining, knowledge discovery, cognitive computing, and data modeling. We construct a big data platform integrating management, sharing, innovative applications, and services. We provide practitioners with accurate plant protection information, such as identification and auxiliary diagnosis of agricultural disasters, prediction of agricultural disasters, and plant protection knowledge. We overcome temporal and geographical restrictions using the Internet to help practitioners solve the difficult problems of plant protection in production in real time. This measure can reduce economic costs, reduce operation intensity, and improve the timeliness of prevention and control. Finally, we provide suggestions to address the shortcomings of current big data platforms for plant protection. In the future, it will be necessary to supplement the platform with further informational data that it lacks, such as data on remote sensing, meteorology, and soil; increase the number of diseases, pests, and weeds in the database; improve the data sharing level; optimize the data analysis technology; strengthen data application and promotion; and improve data security guarantees, become an important part of smart agriculture

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    Practice in the CoreTrustSeal Certification of World Data Center
    Juanle Wang,Yi Wang,Kun Bu,Mingming Wang,Yanjie Wang
    Journal of Agricultural Big Data   2019, 1 (3): 71-81.  DOI:10.19788/j.issn.2096-6369.190307
    Abstract545)   HTML14)    PDF (1632KB)(407)      

    A scientific data center is a vehicle for scientific data management, and assessment and certification are very important for the scientific data management of scientific data centers. The Data Seal of Approval (DSA) established mechanisms to enable trusted digital repositories to provide core level certification for data repositories. Subsequently, the World Data System (WDS) of the International Science Council and DSA jointly launched the CoreTrustSeal partnership certification and international scientific data center certification worldwide. With the Measures for Managing Scientific Data released in March 2018 by the State Council of China, China's scientific data centers are facing the urgent need to improve their level of professionalism and international influence. Hence, this paper analyzes the three categories of articles and 16 requirements for CoreTrustSeal certification, introduced in the actual certification document of the World Data Center for Renewable Resources and Environment (WDC-RRE), which was established in 1988. It also details the experiences of certification and suggests that a scientific data center should strengthen its top-level design, pay attention to long-term data cataloging, improve the level of internationalization, strengthen the preparation of certification supporting materials, and advance the online accessibility of certification materials. After submitting its application in April 2018, the WDC-RRE became the first CoreTrustSeal-certified Trustworthy Data Repository in the geosciences field in Asia in February 2019.

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    Analysis of the Project Approval of NSFC from Three Academies of Agricultural Sciences under the Ministry of Agriculture and Rural Areas from 20112019
    Lei Fang, Xijuan Li, Yan Zhao
    Journal of Agricultural Big Data   2020, 2 (1): 84-92.  DOI:10.19788/j.issn.2096-6369.200111
    Abstract452)   HTML6)    PDF (672KB)(405)      

    To analyze the scientific research strength and present situation of China's agricultural basic research, and reveal the direction of China's agricultural basic research since the 12th Five-Year Plan.


    Using the Letput platform, this paper presents a statistical analysis of the total number of NSFC projects, the number of different types of projects, and the projects of different years and different departments of the Chinese Academy of Agricultural Sciences (CAAS), Chinese Academy of Fishery Sciences (CAFS), and Chinese Academy of Tropical Agricultural Sciences (CATAS) from 2011–2019. In addition, project approval in the first-level and second-level disciplines of the life sciences departments with the largest number of projects has been studied in depth.


    The ability of CAAS to obtain NSFC support is significantly stronger than that of CATAS or CAFS, and the development of disciplines is more comprehensive; CATAS and CAFS have distinct characteristics in the field of agricultural scientific research; CAFS is heavily dependent on the development of aquaculture, and aquaculture is involved before, during, and after production, forming a relatively complete industrial chain; CATAS focuses on tropical plants. In recent years, the number of youth fund projects of the three academies of science has declined significantly.


    Since the 12th Five-Year Plan, the three academies of agricultural sciences have developed rapidly in the field of agricultural basic research. In terms of research direction, CAAS is more comprehensive, and the characteristics of CAFS and CATAS are more distinct. The three academies of agricultural sciences should focus on remain at the cutting edge of world agricultural science and technology, strengthen the cultivation of young talent, and shoulder the important tasks of China's agricultural science and technology innovation.

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    Big Data Portal Development in Agrobiodiversity: Current Research and Future Outlooks
    Zheping Xu,Zengting Shao,XueJun Zhu,Fang Wang,Yuanyuan Wang,Man Xiao,Keping Ma
    Journal of Agricultural Big Data   2019, 1 (2): 76-87.  DOI:10.19788/j.issn.2096-6369.190207
    Abstract653)   HTML52)    PDF (846KB)(389)      

    Agrobiodiversity refers to the variety and variability of animals, plants, and microorganisms used directly or indirectly for food and agriculture, including crops, livestock, forestry, and fisheries. The underpinning of the entire agricultural system and an important part of agricultural production informatization, agrobiodiversity is also the basis of national resource strategies and national security. Although data and knowledge related to agrobiodiversity have been obtained from various research projects, several problems still exist; these include scattered data, lack of top-level designs, inadequate data standards, insufficiently interoperable systems, slow response, and few high-quality think tanks and data portals. To improve research and development on a big data portal for agrobiodiversity in China, we describe advances in agrobiodiversity big data in China and abroad in terms of research data platforms (basic, crop, livestock, forestry, and fishery data platforms; traditional cultural knowledge and think tank platforms; and assessment indicators) and basic resource objects (taxonomy, thesaurus, metadata standard, ontology, and scientific research workflow). In addition, we suggest a system architecture comprising four levels, namely, basic, resource, organizational, and application levels. Finally, we provide a future outlook on the construction and resource sharing of agrobiodiversity data platforms in China.

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    The Application and Future of Big Data in Digital Agriculture in Heilongjiang Province
    Kebao Bi Hongwen Li Yang Lu Zhongjun Zhang Dongmei Lv Zhiqun Liu, Yu Wang Xiaonan Zhang
    Journal of Agricultural Big Data   2020, 2 (1): 21-28.  DOI:10.19788/j.issn.2096-6369.200103
    Abstract637)   HTML21)    PDF (552KB)(376)      

    In the 21st century, Digital Agriculture is emerging as a direction for the future development of agriculture. This approach improves the traditional agricultural production mode from extensive to intensive production, using data intelligence and scientific processes. These days, countries all over the world promote digitalization as an important driving force for agricultural innovation and development, and have made forward-looking deployments in cutting-edge technology research and development, open sharing of data, and personnel training. The United States, Europe and other developed countries are vigorously developing big data to promote modern agricultural practices and strengthen their agricultural development capabilities. The Chinese government also attaches great importance to the role of big data in economic and social development. Since 2014, and supported by the national strategic deployment, 863 plan, the National Natural Resources Fund and other policies and projects, advances have been made in the construction of agricultural basic data, scientific research data, single variety whole industrial chain data, and agricultural product monitoring and early warning platforms. Big data lies at the core of these efforts and is a key element and driving force of digital agriculture. As an important commodity grain base in China, Heilongjiang Province ranks first in terms of grain output and commodity rate, and plays an important role in national food security. As a modern agricultural base, the province applies big data in digital agriculture, and demonstrates concrete achievements such as the construction of a big data platform, the use of sensor data for agricultural monitoring and early warning, the construction of Internet of Things, e-commerce cluster, ect. This paper explores the application of big data in digital agriculture. It defines digital agriculture and big data, summarizes the application of big data in digital agriculture in China and abroad, and analyzes challenges in applying big data in digital agriculture in Heilongjiang Province. It aims to provide guidance for practical applications of big data in digital agriculture.

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    Progress in the Application of Big Data in fishery in China
    Jinxiang Cheng, Yingze Sun, Jing Hu, Xue Yan, Haiying Ouyang
    Journal of Agricultural Big Data   2020, 2 (1): 11-20.  DOI:10.19788/j.issn.2096-6369.200102
    Abstract576)   HTML17)    PDF (615KB)(375)      

    Big data has become an essential resource for green fisheries, and is an important focus for innovation in fisheries science and technology. Big data promotes the production, operation, management, and service provisions of fisheries, and plays a pivotal role in advancing the integration of primary, secondary, and tertiary industries in fisheries. China is the largest aquaculture country in the world, and attaches great importance to research and application development of big data in modern fisheries. For historical and practical reasons, fisheries big data is characterized by diversified resource channels, complex structures, uneven quality, wide application scope, and low overall data quality. Therefore, for research on, and application of fisheries big data in China, it is important to review the progress regarding the application of such data systematically, and clarify the direction of future development. Based on literature research and related scientific research practices, this article compares and analyzes the definitions of fisheries big data by different scholars; elaborates on the concept of fisheries big data; introduces the multiple sources and main characteristics of fisheries big data; and reviews the management and policy progress of fisheries big data. The development and application of fisheries big data in recent years have focused mainly on scientific research, aquaculture management, resource investigation, and economic circulation. In combination with engineering practice, this article explores the scenario application of fisheries big data. Finally, based on the current situation, it identifies the problems and challenges, and provides suggestions for further promoting the development of fisheries big data in China.

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    Development and Practice of the National Earth System Science Data Center in China
    Yaping Yang, Yi Wang, Yan Bai, Xiafang Yue, Jia Du, Yongqing Bai, Jiulin Sun
    Journal of Agricultural Big Data   2019, 1 (4): 5-13.  DOI:10.19788/j.issn.2096-6369.190401
    Abstract466)   HTML11)    PDF (1080KB)(375)      

    Earth system science is an interdisciplinary research area at the frontier of interaction and change mechanisms between the earth's spheres. The research area has accumulated scientific data resources through long-term research on global environmental change and human-earth relationships. To promote the sharing and flow of earth system science data resources and realize their value-added, the National Earth System Science Data Center reflects on the organization and methods of earth system science data sharing since the beginning of the 21st century. Building on the successful development and construction of the Earth System Science Data Sharing Network in China and the National Earth System Science Data Sharing Infrastructure in China, the National Earth System Science Data Center has become one of 20 national science data centers recognized by the Chinese government in 2019, evidencing a major leap from its initiation, and from domestic to international. This paper traces the origins of earth system science and reviews the development of the National Earth System Science Data Center in China. The paper then summarizes the research practices of the National Earth System Science Data Center in earth system science data sharing, highlighting data sharing for standard system construction, data resource management and sharing, and system platform construction, as well as research progress on key technologies and sharing services. Finally, suggestions are offered for sustainable development by the National Earth System Science Data Center .

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    Design of an Intelligent Control Platform for Agricultural Inputs based on Blockchain
    Jianping Qian, Qiangyi Yu, Yun Shi, Baohui Zhang, Yan Zha, Wenbin Wu
    Journal of Agricultural Big Data   2020, 2 (2): 38-46.  DOI:10.19788/j.issn.2096-6369.200204
    Abstract386)   HTML10)    PDF (1221KB)(362)      

    It is very important to improve the control of agricultural inputs to ensure food safety and achieve the goals of reducing fertilizer and drug use. Because of the wide variation in time and space in the supply chain, various regulatory bodies, and limited technical means, some problems occur frequently in the management and control of agricultural inputs. Such problems include irregular accounts, unclear application records, inaccurate information records, and incoherent life cycles. Blockchain technology has the characteristics of decentralization, de-trust, and collective maintenance. It has been applied in many fields such as digital finance, the Internet of Things, intelligent manufacturing, supply chain management, and digital asset trading. Using blockchain technology and the "production–management–application" life cycle of agricultural inputs, this paper proposes a framework for an intelligent management and control platform for agricultural inputs. The framework is composed of data, network, consensus, contract, and application layers. Considering the amount of data that would be handled and the need for security and confidentiality, a dual-mode data storage mechanism consisting of "database and blockchain" was designed. On-chain and off-chain data are associated using a mapping mechanism. Intelligent contracts such as user authentication, qualification verification, account analysis, application warning, and anti-counterfeiting traceability contracts are presented, and their construction is described. Incorporating the advantages of an alliance chain, Hyperledger was used to build the platform. To meet the needs of different participants and be convenient to use, the platform is a combined application system and mobile application, which provides production management, distribution management, application management, intelligent supervision, blockchain management, and other modules. The platform not only enhances the supervision of agricultural inputs, but also improves traceability. Therefore, it provides strong technical support for the regulatory compliance, legal management, and rational use of agricultural inputs.

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    Establishing a Peanut Big Data Platformin China: A Proposal and Applications
    Hui Feng Xiao Wang Laigang Li Guoqiang Qiao Lu Wei Dong Zheng Guoqing Zhang
    Journal of Agricultural Big Data   2020, 2 (1): 45-52.  DOI:10.19788/j.issn.2096-6369.200106
    Abstract441)   HTML20)    PDF (1022KB)(357)      

    China's peanut industry includes activities such as production, processing, import and export for consumption of China’s peanuts around the world ranking first, which is in a pivotal position in the international community. However, China's peanut industry exhibits weak international competitiveness. Better industry data are seen as part of the solution. Having effective data about single agricultural products along each whole industry chain is viewed as an important solution for advancing agricultural supply-side structural reform, and could improve the efficiency of the single agricultural product industry chain. For example, the application of single agricultural product big data such as the “zdscxx-pg” -China Apple Big Data Platform plays an important role in the development of China's agricultural superiority industry . Although peanuts are an important agricultural product of China, there is no peanut big data platform of the whole industrial chain open to the public. This paper focuses on how to build a peanut big data platform, which could help the peanut industry improve efficiency and sustainability assisted by big data. In this paper, we first summarize the actual experience of setting up a data platform for a single product, and analyze the key issues. We identify the different types and sources of relevant data, and outline the platform scheme. Finally, we build the overall structure for a peanut big data platform, and propose practical applications of the platform data. Our research shows the value of a peanut big data platform that covers the whole industry chain, gathers and shares data resources of the peanut industry, grasps the situation of peanut industry, relies on the platform to provide accurate services to all links and entities in the peanut industry, integrates large-scale peanut industry companies into quality and safety traceability and its importance for the sustainable development of China’s peanut industry. It can assist the government in formulating a development strategy for the peanut industry. It can gather data along the whole industry chain and share real-time data with industry participants. A peanut big data platform can reveal peanut industry conditions that are helpful to improve peanut quality, such as optimizing peanut planting layout and promoting producer-seller connections.

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    Preliminary Study on the Application of Blockchain in Data Management of Crop Germplasm Resources
    Haiyang Liu,Wei Fang,Yanqing Chen,Yongsheng Cao
    Journal of Agricultural Big Data   2019, 1 (2): 105-113.  DOI:10.19788/j.issn.2096-6369.190209
    Abstract691)   HTML29)    PDF (1093KB)(355)      

    Crop germplasm resources are the strategic resources of our country and the fundamental material for safeguarding national food security and developing modern seed industry.The past 70 years have witnessed China have formed a systematic work of crop germplasm resources,including investigation and collection, evaluation and identification, cataloging and storage, and sharing and utilization. With the rapid development of computer technology, using information technology to promote the standardization of crop germplasm resources’ work is the prospective trend of germplasm resources work. The past 30 years have witnessed China has established a complete database of germplasm resources and information systems for germplasm resources’ work.For the difficulty in traceability of germplasm data, transmission, and property rights disputes in the current germplasm resources, this paper proposes a blockchain-based solution and studies the germplasm resources data Management plan, storage plan, consensus plan and encryption plan based on blockchain . Combined with the current development of germplasm resources’ work and blockchain technology, this paper analysis the prospects of blockchain technology using in the field of germplasm resources.

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    Research and Application of Citrus Big Data
    Qiuzi Wen‑Han, Yongqiang Zheng, Yang Liu
    Journal of Agricultural Big Data   2021, 3 (1): 33-44.  DOI:10.19788/j.issn.2096-6369.210104
    Abstract578)   HTML20)    PDF (1772KB)(355)      

    Using the citrus industry chain model as the basic framework, this paper proposes an explicit definition of Citrus Big Data. For each of the four main parts of the citrus industry chain, i.e., production resources, planting operations, processing, storage and transportation, and marketing, the composition of data, acquisition methods and challenges in applications of its core data resources are analyzed, respectively. Applications of typical data technologies such as product standardization, image recognition, meteorology forecasting, data visualization, and digital traceability in the citrus industry are systematically reviewed. Cases studies on Coca Cola orange juice, Chongqing citrus and Gannan umbilical orange are presented, demonstrating that big data technology is playing a more and more important role in the citrus industry, aiding green farming, disease and insect pest control, production increase, and improvement of fruit commodity rates. In China, research on citrus has entered a stage of rapid development since 2007, and has made great progresses in talent training, fundamental research, and transformation and application of scientific research results, but research on the application of the new generation information technologies such as big data and artificial intelligence, is still limited. Major citrus production regions in southern China are actively exploring the feasibility route of digital transformation for citrus industry. As a core resource for industrial upgrading and digital transformation, Citrus Big Data has a wide range of applications and great potential for growth. Application of Citrus Big Data in China is still in its infancy, facing many challenges such as equality in science and technology, data sharing, development of key models and algorithms, etc.

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    The Exploration and Practices on Data Management in Forestry Science
    Ping Xiao Yundan Hou Ruixia Ji
    Journal of Agricultural Big Data   2019, 1 (3): 46-56.  DOI:10.19788/j.issn.2096-6369.190305
    Abstract433)   HTML12)    PDF (593KB)(348)      

    For several years, the National Forestry Science Data Sharing Service Infrastructure (NFSDSSI) has established a forestry science data system, developed a software and hardware system to offer data on the Internet, and daily it operates data sharing services that are geared to the needs of science and technology innovation, economy and social development, and all of society. This paper describes the hierarchical and classified management of forestry science data according to the investigations and practices of NFSDSSI’s developments and service operations. The forestry science data framework consists of 12 first-level categories in accordance with forestry science subjects, and a forestry science data system has been established. The forestry science datasets are divided into three levels according to the relevant laws and regulations. Hence, open-level datasets can be shared conveniently, restricted-level data sets can be shared in a controlled manner, and proprietary-level data sets can be shared securely. This paper outlines the strengthening and implementation of the main responsibility of the NFSDSSI’s support organizations. In particular, rules and regulations have been explored, developed, and implemented; a series of technical standards have been formulated to normalize the collection, processing, storage, and data quality assurance of data sets; and software and hardware equipment, financing, and personnel for data management and operations have all been obtained. This paper also introduces the platform structure and service operations for sharing data on the Internet. The established data storage, management, and service operation systems; online and offline data sharing; thematic data sets and data products; technical support services for user data applications; and special subjects for important requirements are now all available to users. This paper summarizes and analyzes the effectiveness of data management, various structures, and service operations of the NFSDSSI, and proposes new ideas for the NFSDSSI’s work to further strengthen and standardize forestry scientific data management and ensure scientific data security.

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    Advances in the Study of Domestic and Foreign Scientific Data Management Methods
    Yongqing Bai,Yaping Yang,Jiulin Sun
    Journal of Agricultural Big Data   2019, 1 (3): 5-20.  DOI:10.19788/j.issn.2096-6369.190301
    Abstract764)   HTML49)    PDF (966KB)(348)      

    Scientific data are important strategic resources in the information age. Efficient management and wide circulation can critically enhance the value of scientific data resources. Rapid information technology developments and large investments in science and technology projects have led to an explosion in the number of scientific data resources, which poses a greater challenge to scientific data management. The transformation from industrial society to information society increases the importance of scientific data management, domestically and internationally. Many data management institutions and government departments promote robust scientific data management and sharing through the construction of data clusters, improvement of security measures, optimization of development concepts, and increased funding. This paper analyzes and summarizes the advanced experience of international scientific data management, based on a comprehensive survey of the concepts, policies, practices and achievements of scientific data management at domestic and foreign institutions. It proposes future directions and suggestions for the development of scientific data management in China. Recommendations for the future include the following: (1) Continuously standardize and improve the management of various scientific data resources to ensure a mechanism to improve the standardization level. (2) Strengthen deep mining of data resources to realize the transformation from data to information, knowledge, wisdom and decision-making. (3) Strengthen data science and technology talents training, implement data scientist programs from the government level, and provide talent support for scientific data management. (4) Broaden international cooperation channels, strengthen cooperation, promote the international influence of existing national science data centers, provide strategic guidance for the development and construction of data science in China, and build core competitiveness to enhance the comprehensive national strength in the information age.

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    Application and Prospects for Big Data of Traditional Chinese Medicine Resources in Inner Mongolia
    Mingxu Zhang, Ru Zhang, Tuya Xilin, Yuan Chen, Yaqiong Bi, Chunhong Zhang, Taotao Wu, Minhui Li
    Journal of Agricultural Big Data   2021, 3 (2): 42-53.  DOI:10.19788/j.issn.2096-6369.210205
    Abstract529)   HTML23)    PDF (3449KB)(334)      

    “Big data” refers to a huge information collection that has four characteristics: large data volume; complex type; low value density; and high effectiveness. Big data technology is a non-structured data processing technology that can efficiently handle large volumes of data collected by different industries. “Big data of traditional Chinese medicine resources” relates to the large volumes of data with practical significance that are generated during the long development of China’s traditional medicine industry. Inner Mongolia is a region where ethnic minorities live in concentrated communities. With their long historical development, both traditional Mongolian and Chinese medicine have come to play an important role in disease prevention and treatment; considerable amounts of traditional Chinese and Mongolian medicine data have accumulated. One of the most important forces for the current development of resource-related industries with respect to traditional Chinese medicine is how to collect and organize these data is currently an important one in the development of resource-related industries with respect to traditional Chinese medicine; which is also a driving force in the modernization of such medicine. Following scientific and technological development, combining traditional Chinese and Mongolian medicine resources with cutting-edge big data technology offers an effective means for modernizing such medicine. In the present study, we summarizes the construction process of a database was created for traditional Chinese and Mongolian medicine resources and a big data platform for the traditional Chinese medicine industry in Inner Mongolia. This paper also summarizes the results related to the application of big data technology for traditional Chinese and Mongolian medicine resources in that region. At last, a proposal is made for dealing with problems that may be encountered in developing traditional medicine Chinese and Mongolian resources in Inner Mongolia. The research method adopted in the present study can be applied to the sustainable use of traditional Chinese medicine resources. That approach can also address the deficiencies with traditional Chinese medicine and ethnic medicine in different parts of China. This study offers solutions related to research into the application of big data technology for traditional Chinese medicine resources in Inner Mongolia as well as other parts of the country. In that way, this article presents a scientific foundation for applying big data technology for traditional Chinese medicine resources in the future.

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    Analysis and Modeling of the Heterogeneous Distribution Phenotyping Characteristics of Cucumber Leaves
    Tingting Qian,Shenglian Lu,Xiuguo Zheng,Jingyin Zhao,Jian Wang,Juan Yang
    Journal of Agricultural Big Data   2019, 1 (2): 41-49.  DOI:10.19788/j.issn.2096-6369.190204
    Abstract432)   HTML17)    PDF (3566KB)(317)      

    [Objective] The orientation and distribution characteristics of the cucumber leaf blade are important structural parameters that determine the light interception and photosynthesis capacity of the canopy. Detailed analysis of leaf azimuth distribution with different cultivation densities will determine the environmental factors that affect leaf distribution and canopy heterogeneity structural characteristics, and support the study of the response mechanism of plant morphology to the environment. [Methods] To study the response of leaf distribution to the light environment under light gradient changes inside the canopy, nine sets of measurement test data under five density treatments were used. A three-dimensional scanning method was used to digitally collect the phenotypic parameters of cucumber canopy organs, and the heterogeneous characteristics of leaf distribution were quantitatively analyzed in different azimuth regions. [Results] Based on the detailed analysis of the relationship between leaf azimuth distribution and solar elevation angle and leaf area index, correlation models were established. The difference in azimuth distribution frequency between the inside and outside of double rows was amended using the cosine function to simulate the response of azimuth distribution frequency to man-made cultivation management. The accuracy of the model was verified by the measured data, and the accuracy rate reached 0.89. [Conclusion] In this study, the response of the azimuthal distribution frequency to canopy growth, light environment and planting management was implemented. It provides an important reference for the acquisition of cucumber canopy phenotypic parameters, and lays the foundation for more accurate cucumber functional and structural plant model construction.

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    Analysis of the Price Fluctuation Characteristics of Chinas Wheat Market Using Big Data: A Case Study of Shandong Province
    Xiaoyan Zhang, Li Meng, Lili Wang, Feng Liu, Jiajia Liu, Decheng Lu
    Journal of Agricultural Big Data   2020, 2 (3): 75-83.  DOI:10.19788/j.issn.2096-6369.200309
    Abstract311)   HTML13)    PDF (1515KB)(316)      

    Wheat is one of the three major grain crops in China. To ensure the safety of our country's rations, the state monitors the area and yield of wheat, as well as the price. The stability of the price of grain is related to the development of the national economy and social stability. With the gradual deepening of grain market-oriented reform, the price of wheat presented a period of change. Therefore, it is necessary to understand the forces governing price changes in the wheat market and to use the characteristics of market fluctuations to control the price of wheat and other grains. We found that the trend of the change in wheat price in China was nearly identical to that in Shandong Province. Therefore, this paper took Shandong Province as an example to study the characteristics of wheat price change in China. Using the time-series decomposition method, we analyzed the characteristics of the monthly actual price of wheat in 2009–2019 in Shandong Province. We found that the wheat price in Shandong Province showed a long-term trend of straight-line rising and then steadily declining with obvious periodic fluctuation. After removing commodity price level, the change of wheat price in Shandong could be divided into three periods of fluctuation with an average length of 44 months each. The wheat price in Shandong Province could be affected by seasonal factors and also national policy. A macroadjustment could be conducted for the wheat market by using wheat market fluctuation rules. We suggest that the state should fully consider the wheat price trend along with the law of economic and social development, and adjust the wheat and other grain markets in accordance with the periodic fluctuation law of the wheat market price, so as to allow the wheat price to change within a reasonable range. From a development perspective, the state should promote the reform of the wheat minimum purchase price policy, gradually improve the supporting policies of grain production, and strengthen research on agricultural monitoring and early warning.

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    Spatiotemporal Characteristics of Thermal Resources in Sichuan Province Against the Background of Climate Change
    Wenbo Gao, Peng He, Zhengyu Lin, Yuan Zhang
    Journal of Agricultural Big Data   2020, 2 (1): 60-69.  DOI:10.19788/j.issn.2096-6369.200108
    Abstract653)   HTML9)    PDF (2228KB)(312)      

    This study attempts to identify the variation trend of agricultural thermal resources and characteristics of trend of all regions in Sichuan Province against the background of global warming.


    Based on the daily meteorological data from 40 meteorological stations from 1961 to 2016, the thermal change characteristics of Sichuan Province were studied. We adopted the climatic tendency rate and the nonparametric Mann–Kendall statistical test to identify the temporal changes in the Sichuan Province thermal resources. Multiple regression equations for the thermal resource factors were constructed based on their longitude, latitude, and altitude. We also used a multivariate regression model with the residual modified by inverse distance weight interpolation to analyze the spatial variation of thermal resources.


    The results showed that: the thermal conditions in Sichuan Province generally increased over the past 56 years and the annual average temperature, the annual average minimum temperature, the accumulated temperature above 0°C, and the accumulated temperature above 10°C had all increased by 0.204°C/10a, 0.267°C/10a, 58.48°C/10a, and 61.49°C/10a, respectively. The spatial distribution showed that the annual average temperature, the annual average minimum temperature, accumulated temperature above 0°C, and accumulated temperature above 10°C expanded from the southeast to northwest in Sichuan Province. The areas where the incremental changes in the annual average temperature and annual minimum temperature were the most significant were the northwest region of Sichuan Province, the Panzhihua-Xichang region, and the east region of Sichuan basin. The accumulated temperature above 0°C in the western Sichuan plain had increased significantly, while and the southeast region of Sichuan Province had the largest increase in accumulated temperature above 10°C. From 1961 to 2016, the frost-free period and the growing season for thermophilic crops became longer in Sichuan Province. The most obvious extended regions of the frost-free period were the northwest region, western Sichuan plain, and the northeast region of Sichuan Province. The area with significant extension of the growing season of thermophilic crops was Liangshan in the southwest region of Sichuan Province, where the thermal conditions for thermophilic crops improved gradually.


    In the past 56 years, the temperature in Sichuan Province had generally increased, while the frost-free period and the growing season of warm crops were also extended. In general, agricultural thermal resources have increased in Sichuan Province and thermal resources have become more abundant in Sichuan basin, Pan-Xi region, and the northwest Sichuan plateau.

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    Design and Implementation of Animal Quarantine Certificate Blockchain Application System
    Linyi Zhong, Xiangbao Meng, Honggang Liu, Chaoyang Liu, Yang Wang
    Journal of Agricultural Big Data   2020, 2 (2): 84-93.  DOI:10.19788/j.issn.2096-6369.200209
    Abstract426)   HTML12)    PDF (1646KB)(309)      

    Animal quarantine is a crucial link in food safety supervision; electronic certification of animal quarantine is highly important toward ensuring food safety. An animal quarantine certificate confirms the safe market conditions for animal husbandry, poultry, and meat products. Such certification efficiently presents information about the state of quarantine; it provides comprehensive tracking of the flow of animals and their products in addition to disease tracing and management responsibility. That certification is vital for food safety and community health. The present study examined ways to enhance anti-tampering and anti-counterfeiting measures with respect to the traceability function of animal quarantine certificates; it aimed to eliminate operational risks and potential food safety hazards during the process of certificate replacement. This paper did so by employing the features of blockchain technology (such as decentralization, anti-tampering, openness, independence, and traceability): it utilized the underlying technical framework of Hyperledger Fabric. An application system for animal quarantine blockchain was designed. A consortium blockchain network was established to link farms, slaughterhouses, distribution stores, and regulators; the chain flow of business processes was followed; quarantine personnel, farms, and other relevant parties jointly recorded invoice and transaction information on the blockchain ledger. Decentralization and inability to tamper with information ensure the safety and transparency of the process of issuing and changing certificates. The department responsible for supervising animal epidemic prevention and quarantine can acquire the supervision node to monitor the entire process of issuing and changing quarantine certificates. This system effectively improves the security and traceability of quarantine applications; it reduces process vulnerabilities, and it improves risk management and control capabilities. Using blockchain technology, this system provides credible, reliable animal quarantine certificates in addition to certificate replacement services. The system integrates technical trust with deposit trust. Thus, blockchain technology is becoming an important technological means to ensure the safety of meat products.

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