Most Download Articles

Published in last 1 year | In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year|

All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Analysis of China's Rural E-commerce Research Dataset JIA Cheng, YI HongMei Journal of Agricultural Big Data    2023, 5 (4): 95-102.   DOI: 10.19788/j.issn.2096-6369.230412 Abstract （2628）   HTML （182）    PDF（pc） （359KB）（20141）       Knowledge map    Save This study reviewed the data used in the studies on rural e-commerce in China. The rural e-commerce data are divided into two types of datasets based on the characteristics of targeted e-commerce interventions and agricultural product trading locations. The first dataset includes databases on comprehensive demonstration of e-commerce in rural counties, and Taobao Villages and e-commerce index. The second dataset involves databases on e-commerce of agricultural products, and cross-border e-commerce agrarian products. We presented the data sources, the definition of related indicators, and the time span of each dataset, and analyze the pros and cons of each data in answering the related research topics. This systematic review can be a benchmark for researchers interested in rural e-commerce to understand the data and assess the related studies using these datasets. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Genome-wide Identification and Expression Analysis of WRKY Gene Family in Five Legumes CHEN NaiYu, ZHAO He, JIANG HuiXin, LING Lei, YIN YaJie, REN GuoLing Journal of Agricultural Big Data    2023, 5 (2): 16-26.   DOI: 10.19788/j.issn.2096-6369.230204 Abstract （1089）   HTML （31）    PDF（pc） （4374KB）（10453）       Knowledge map    Save In order to enhance understanding of the diversity and evolution of WRKY genes in leguminous plants, and to explore the functions of WRKY transcription factor family members and their applications in breeding, in this study, we analyzed the classification, basic physicochemical properties, evolutionary relationship, gene structure, chromosome location, conserved motifs, promoter elements, gene collinearity, expression in five legumes (Glycine max, Cicer arietinum, Phaseolus vulgaris, Medicago truncatula, Lotus japonicus) by using bioinformatics. A total of 185, 61, 90, 108 and 83 WRKY genes were identified, respectively. WRKY protein were identified, and the classification, basic physicochemical properties, evolutionary relationship, gene structure, chromosome location, conserved motifs, promoter elements, gene collinearity, expression analysis were systematically analyzed.The WRKY proteins in all five species were divided into three classes and five subclasses.The WRKY proteins derived from the same evolutionary clade were found to have similar genes and protein structures. There are gene replication events in members of WRKY gene family of the five leguminous plants, and there are significant differences in expression in each tissue.The expression patterns of WRKY genes in different tissues indicate that they may play an important role in the growth and development of leguminous plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Tomato Dataset for Agricultural Scene Visual-Parsing Tasks Lingli Zhou, Ni Ren, Wenxiang Zhang, Yawen Cheng, Cheng Chen, Zhongyi Yi Journal of Agricultural Big Data    2021, 3 (4): 70-76.   DOI: 10.19788/j.issn.2096-6369.210408 Abstract （2278）   HTML （134）    PDF（pc） （1003KB）（8737）       Knowledge map    Save Agricultural robots are an important part of the development of agricultural modernization, and computer vision technology effectively promotes their application in the field of agriculture by perceiving and analyzing crops and the environment. However, because of the complexity and diversity of agricultural scenes, the detailed and annotated large-scale image datasets required by advanced computer vision methods are scarce in the field of agriculture. This lack of datasets is the main challenge in the development of computer vision technology in the field. To solve this problem, this paper presents a large-scale tomato image dataset that can be used for semantic image segmentation, instance segmentation, target detection, and other tasks. The dataset consists of synthetic and real images. The synthetic images include 3250 synthetic tomato images and the corresponding pixel-level semantic segmentation label images; the real images consist of 750 monocular images and 400 binocular images taken by RGB cameras, some of which have detailed manual labels for instance segmentation and target detection. This research aims to enrich many aspects of the dataset, including its capacity, the dimensionality of the annotation information, and the complexity of the scene, and to provide data support for solving future problems in the field of agriculture using computer vision technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research Progress of Multimodal Knowledge Graph in Agriculture Jiayun Chen, Xiangying Xu, Yonglong Zhang, Ye Zhou, Hongjiang Wang, Changwei Tan Journal of Agricultural Big Data    2022, 4 (3): 126-134.   DOI: 10.19788/j.issn.2096-6369.220320 Abstract （2237）   HTML （85）    PDF（pc） （802KB）（8062）       Knowledge map    Save Incorporating entities of multiple modalities and their semantic relationships on the basis of traditional knowledge graph, multimodal knowledge graph provides important information in the form of text, image and sound. It plays an important role in eliminating ambiguity and supplementing visual knowledge. In recent years, under the background of the rapid development of agricultural informatization and intelligence, knowledge graph technology has attracted extensive attention. In this article, the concepts of knowledge graph and multimodality are introduced in detail. Meanwhile, technical methods such as multimodal representation learning are elaborated from the perspective of graph construction. For the applications of multimodal knowledge graph in agriculture, we focus on the research of agricultural intelligent question answering system, plant diseases and pests’ identification, agricultural product recommendation and so on. At the same time, the challenges in construction and development of agricultural multimodal knowledge graphs are prospected and analyzed. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （4383）   HTML （319）    PDF（pc） （1041KB）（7838）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

The Application, Problems and Development of China's Agricultural Smart Sensors Rui Yan, Zhen Wang, Yanhao Li, Zhemin Li, Xian Li Journal of Agricultural Big Data    2021, 3 (2): 3-15.   DOI: 10.19788/j.issn.2096-6369.210201 Abstract （3233）   HTML （151）    PDF（pc） （1468KB）（7224）       Knowledge map    Save Agricultural smart sensors are among the key technologies of intelligent agriculture. This paper describes the concept, characteristics, and implementation methods of smart sensors and introduces the composition, development, and application of agricultural smart sensors. The agricultural smart sensors were classified into three categories, based on the type of information they detect: life information, environmental information, and quality and safety sensors. The life information smart sensors detect plant and animal life information, and the environmental information smart sensors detect information about water, soil, livestock and poultry, and meteorological events. Currently, the application of agricultural smart sensors in China faces several problems. These include a low degree of integration (modular implementation), a heavy reliance on imports for the core components of agricultural smart sensors (sensor components and microcontroller), a low degree of intelligence, and limited application scope. The root causes of these problems mainly lie in the lack of core controllers dedicated to agriculture, the lack of self-developed high-end agricultural sensors, and the lack of dedicated wireless communication network protocols and high-precision smart sensor algorithms. The paper proposes some feasible countermeasures, such as manufacturing China’s “agricultural core” and high-performance MEMS sensors, constructing special agricultural wireless networks, and developing high-performance smart algorithms. If implemented, these countermeasures will help promote the intelligent manufacturing of agricultural smart sensors in China. With the rapid development of smart agriculture, China’s smart manufacturing of agricultural smart sensors is crucial. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

The Agricultural Pest and Disease Image Recognition Dataset in Nanjing, Jiangsu Province, in 2023 WANG BoYuan, GUAN ZhiHao, YANG Yang, HU Lin, WANG XiaoLi Journal of Agricultural Big Data    2023, 5 (2): 91-96.   DOI: 10.19788/j.issn.2096-6369.230214 Abstract （3920）   HTML （456）    PDF（pc） （4768KB）（6162）       Knowledge map    Save Agricultural pests and diseases pose a serious threat to crop yield and quality, making accurate and efficient detection and identification of pests and diseases crucial in agricultural production. In this paper, we propose a comprehensive agricultural pests and diseases dataset, which includes agricultural pest detection dataset, agricultural disease detection dataset, agricultural disease classification dataset, and rice phenotype segmentation dataset. By collecting and curating data from public sources and academic papers, we ensured the diversity and representativeness of the dataset. Rigorous quality control and validation measures were implemented during the data filtering, cleaning, and annotation processes to ensure the accuracy and reliability of the dataset. This dataset can be used for agricultural pest and disease recognition, as well as rice phenotype identification and other agricultural visual tasks. It provides valuable resources for agricultural pest and disease research and contributes to the sustainable development of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Construction Process and Technological Prospects of Large Language Models in the Agricultural Vertical Domain ZHANG YuQin, ZHU JingQuan, DONG Wei, LI FuZhong, GUO LeiFeng Journal of Agricultural Big Data    2024, 6 (3): 412-423.   DOI: 10.19788/j.issn.2096-6369.000052 Abstract （1269）   HTML （98）    PDF（pc） （1315KB）（5751）       Knowledge map    Save With the proliferation of the internet, accessing agricultural knowledge and information has become more convenient. However, this information is often static and generic, failing to provide tailored solutions for specific situations. To address this issue, vertical domain models in agriculture combine agricultural data with large language models (LLMs), utilizing natural language processing and semantic understanding technologies to provide real-time answers to agricultural questions and play a crucial role in agricultural decision-making and extension. This paper details the construction process of LLMs in the agricultural vertical domain, including data collection and preprocessing, selecting appropriate pre-trained LLM base models, fine-tuning training, Retrieval Augmented Generation (RAG), evaluation. The paper also discusses the application of the LangChain framework in agricultural Q&A systems. Finally, the paper summarizes some challenges in building LLMs for the agricultural vertical domain, including data security challenges, model forgetting challenges, and model hallucination challenges, and proposes future development directions for agricultural models, including the utilization of multimodal data, real-time data updates, the integration of multilingual knowledge, and optimization of fine-tuning costs to further promote the intelligence and modernization of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

2022 Inner Mongolia UAV Potato Image Dataset HU Tianci, WANG Ruili, JIANG Chengxiang, BAI Tao, HU Lin, WANG Xiaoli, GUO Leifeng Journal of Agricultural Big Data    2023, 5 (1): 40-45.   DOI: 10.19788/j.issn.2096-6369.230112 Abstract （1036）   HTML （74）    PDF（pc） （911KB）（4687）       Knowledge map    Save Potatoes are the fourth largest food crop in the world, and large-scale planting of potatoes is an important basis for ensuring high yields of potatoes. With the development of digital agriculture, the large-scale planting of potatoes also tends to be automated and intelligent. UAVs are an important tool in crop plant protection and growth monitoring. UAV spectral data play an important role in crop identification and crop growth status analysis. important. In order to explore the role of spectral data and image data in potato growth, this study conducted three different spatial resolution images on two mature seed potato experimental fields in Hulunbeier, Inner Mongolia, on August 13, 16 and 18, 2022. Spectral data and image data are collected. UAV remote sensing was used to obtain multi- spectral images at different heights, and the data of potato leaves on the ground were collected. After manual in- spection and sorting, this dataset was constructed. The spectral data of this dataset is complete and the leaf data is clear, which can provide data support for research on potato crop identification, planting area estimation, and potato-related vegetation index changes on different dates during the maturity period. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Training Dataset for Deep Neural Network Model Recognition of Common Cotton Diseases ZHAO HongXin, SHAO MingYue, PAN Pan, WANG ZhiAo, MU Qiang, HE ZiKang, ZHANG JianHua Journal of Agricultural Big Data    2023, 5 (4): 47-55.   DOI: 10.19788/j.issn.2096-6369.230405 Abstract （972）   HTML （66）    PDF（pc） （3734KB）（4181）       Knowledge map    Save In the realm of cotton disease identification, the Deep Neural Network emerges as a pivotal paradigm. Progress in this sphere hinges on the availability of a comprehensive repository of scientific data, encapsulating a broader spectrum of diseases, variegated soil profiles, and multifaceted environmental attributes. Currently, this dearth of data serves as the principal bottleneck, impeding the advancement of state-of-the-art models and algorithms.Within this scholarly exposition, we present a meticulously curated cotton disease dataset, poised to bridge this knowledge chasm. This dataset comprehensively encompasses four prevalent cotton diseases: anthracnose, bacterial blight, brown spot, and wilt disease. These maladies' exemplars were meticulously gleaned from cotton fields situated in the Potianyang High-standard Farmland Demonstration Base, nestled serenely in Sanya, Hainan Province, China.The dataset unfolds as a magnum opus, comprising 3 453 high-resolution images. These vivid snapshots provide a panoramic view, capturing the pristine vitality of healthy leaves, juxtaposed with leaves beset by disease at various growth stages. The data acquisition, executed with precision, leveraged field random sampling methodologies, ensuring a faithful reflection of the natural complexity in real-world cotton plantations.Every image underwent meticulous scrutiny, with ten seasoned mavens in cotton pathology meticulously overseeing the annotation. An additional cohort of twenty annotators conducted a second round of annotations on randomly selected image subsets, fortifying the dataset's integrity and precision. The Vision Transformer model was employed to guarantee the dataset's resilience and accuracy.This hallowed dataset was meticulously gathered amidst the complexity of field environments, encapsulating the nuances of major cotton diseases in their native habitat. Its high image resolution, akin to an opulent tapestry of visual data, renders it an invaluable resource for pioneering research, astute training, and the relentless validation of astute, intelligent cotton disease recognition models and algorithms. This opulent repository caters to the discriminating tastes of researchers, practitioners, and sagacious decision-makers, furnishing them with a comprehensive and crystalline understanding of the multifaceted tapestry of cotton diseases and their intricate management. Data summary: Item Description Dataset name A Training Dataset for Deep Neural Network Model Recognition of Common Cotton Diseases Specific subject area Agricultural Science, Computer Science Time range December, 2021－August, 2023 Geographical scope This dataset covers the plain planting area of Potianyang Base in Sanya City, Hainan Province, with a central latitude and longitude of (109.165497,18.3931609999999) Data types and technical formats Cotton Image Format *. jpg, Cotton Disease Classification Standard Format *. txt Dataset structure The dataset consists of 3453 image files and one text file. The image files belong to a folder named Cotton Disease Data, all of which are *. JPG files. The folder where the text files belong is named the Cotton Disease Dataset, where all files are *. TXT Volume of data 2.74 GB Data accessibility CSTR：17058.11.sciencedb.agriculture.00029 DOI：10.57760/sciencedb.agriculture.00029 Financial support National Key R&D Plan (2022YFF0711805); Science and Technology Special Fund for Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2023-45);Innovation Engineering of the Chinese Academy of Agricultural Sciences (CAAS - ASTIP - 2023 - AII, ZDXM23011); Special funds for basic research business of central level public welfare research institutes (Y2022XK24, Y2022QC17, JBYW - AII - 2022 - 14, JBYW - AII - 2023 - 06); Sanya Chinese Academy of Agricultural Sciences National South Breeding Research Institute South Breeding Special Project (YDLH01, YDLH07, YBXM10, ZDXM23011, YBXM2312) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1788）   HTML （48）    PDF（pc） （5172KB）（3876）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

An Overview of Zero-Knowledge Proof Technology and Its Typical Algorithms and Tools WAN Wei, LIU JianWei, LONG Chun, LI Jing, YANG Fan, FU YuHao, YUAN ZiMeng Journal of Agricultural Big Data    2024, 6 (2): 205-219.   DOI: 10.19788/j.issn.2096-6369.200002 Abstract （829）   HTML （39）    PDF（pc） （517KB）（3796）       Knowledge map    Save In the context of the increasing importance of data security and privacy protection, Zero-Knowledge Proofs (ZKPs) have provided a powerful tool for protecting privacy. This article comprehensively discusses the technology of zero-knowledge proofs and their application in modern cryptography. First, the article introduces the basic concepts of zero-knowledge proofs, as well as different types of ZKPs such as Snarks and Starks, along with their technical characteristics and application scenarios. In particular, the article conducts an in-depth study of ZK-Snarks. At the same time, the article also discusses other proof mechanisms such as ZK-Stark and Bulletproofs, comparing their differences in design and performance. Then, it focuses on the application of ZKPs in the blockchain environment and analyzes the related tools for writing zero-knowledge proofs. Finally, it points out some potential problems and future research directions in the field of zero-knowledge proofs. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Dataset on the Compiled Materials of Costs and Profits of Rice, Wheat and Corn Products of China ZHAN ZiSen, ZHANG XiaoHeng, CHEN Bo Journal of Agricultural Big Data    2023, 5 (4): 110-117.   DOI: 10.19788/j.issn.2096-6369.230414 Abstract （1691）   HTML （128）    PDF（pc） （362KB）（3732）       Knowledge map    Save General Secretary Xi Jinping has pointed out that "the prosperity of industries is of paramount importance for rural revitalization." The Cost-Benefit Survey of Agricultural Products records information on inputs, outputs, and returns related to agricultural products, serving as the foundation for macroeconomic regulation and price management by government departments. In the new era and on the new journey, this dataset will play an even greater role in advancing the rural revitalization strategy. A large number of literature analyze the situation of China’s agricultural input factor use, productivity, cost and profit based on this dataset, but the introduction of details such as the sample selection of the dataset, the collection process, and the connotation of the relevant indicators needs to be enhanced. Therefore, this paper primarily compiles cost-benefit survey data for three types of grains, including early-season rice, mid-season rice, late-season rice, japonica rice, wheat, and maize, from 2005 to 2017 in 31 regions, forming a comprehensive dataset. This paper provides an introduction to the background, data collection methods, primary content of the data, and its implications and values. The data is collected by using a stratified random sampling procedure to improve the representativeness. The cost and profit data of rice farms contains the farm size, yield, output value, pesticide cost, fertilizer quantity and cost, seed quantity and cost, irrigation cost, labor quantity and cost, and land cost on per unit area. Relevant scholars can not only use the data to analyze the input-output situation of China's agricultural products, but also draw on the sampling method and quality control experience of the data. Data summary: Item Description Dataset name A Dataset on the Compiled Materials of Costs and Profits of Agricultural Products of China Specific subject area Agricultural economics Research topic Agricultural inputs and outputs, productivity Time range 2005—2017 Geographical scope National weighted average and 31 provinces, municipalities and autonomous regions (subject to change depending on the crop structure of each region, see text section for details). Data types and technical formats .xlsx Dataset structure This database contains 6 products for 3 grains, with three types of data for each product, including 28 survey items for the cost-benefit profile, 33 survey items for the cost and labor profile; and 29 survey items for the fertilizer input profile, as described in the main text. All in one excel file. Volume of data 0.7 MB Key index in dataset Yield, output, cost, revenue Data accessibility CSTR: 17058.11.sciencedb.agriculture.00083 DOI: 10.57760/sciencedb.agriculture.00083 Financial support National Natural Science Foundation of China（72003074） Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （3702）   HTML （347）    PDF（pc） （915KB）（3684）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （3383）   HTML （310）    PDF（pc） （2372KB）（3639）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1838）   HTML （111）    PDF（pc） （1011KB）（3368）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1544）   HTML （41）    PDF（pc） （800KB）（3344）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

International Comparative Study on Management Mode of National Science Data Center Mingrui Huang, Guoqing Li, Jing Li, Xiangtao Fan Journal of Agricultural Big Data    2019, 1 (4): 14-29.   DOI: 10.19788/j.issn.2096-6369.190402 Abstract （1025）   HTML （14）    PDF（pc） （933KB）（2956）       Knowledge map    Save This paper presents models of the development, management, evolution of national science data center systems in the United States, the United Kingdom， and China. Our methods include network research and literature analysis to analyze the construction process, management mode and evaluation methods of these science data center sys‐ tems. In the United States, national data centers, domain-level data centers, and resource-node data centers exist and share data in an orderly manner, forming a data flow model from“the capillary to the aortic convergence.”In the United Kingdom, national level and field research data centers form a data flow model with“several parallel aortas”, in which data is directly obtained from the national and domain-level data centers. In China, similar to the US convergence model, national science data centers are established in key areas throughout the country to stan‐ dardize science data management and plan and build science data centers in the regions. The regional data cen‐ ters are encouraged to submit data to national data centers, thereby promoting the flow of scientific and technological resources from the relevant fields to the national platforms for convergence and integration. Our paper also considers the adjustment list released by the National Science Data Centers of China in June 2019, discussing the ecological correlation in scientific data management of National science data center and its science data management model relative to the new model that China's science data management may face. It is argued that the National Scientific Data Centers will play an important role in promoting the development of big science in China and provide support for the development of science and technology in the era of big data. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion DU JiaKuan, LI YanFei, SUN SiWen, LIU JiDong, JIANG TengDa Journal of Agricultural Big Data    2024, 6 (1): 56-67.   DOI: 10.19788/j.issn.2096-6369.000010 Abstract （674）   HTML （40）    PDF（pc） （9692KB）（2944）       Knowledge map    Save Traditional methods of rice phenological phase feature extraction based on time-series remote sensing images require high temporal resolution, which is difficult to meet due to imaging conditions. Due to the different environmental conditions in different rice growing regions, the rice planting area extraction method based on single image has poor generalization ability. In this paper, similar optical and Synthetic Aperture Radar (SAR) data were selected to reduce the spatiotemporal information differences in rice planting area images. The spatial feature information of optical data and backscatter information of SAR data were effectively used to extract rice features by using a two-structure network model through pan-spatio-temporal feature fusion. Experiments show that the overall test accuracy of the training model validation set on the rice datasets of Sanjiang Plain and Feixi County is 95.66%, and the Kappa coefficient is 0.8805. The results of rice extraction in Nanchang City were in good agreement with the actual field boundaries, and the overall extraction accuracy was 86.78%, which proved the generalization ability and practicability of the pan-temporal feature model. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1935）   HTML （62）    PDF（pc） （2358KB）（2847）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Review of Agricultural Supply Chain Management and Its Prospects for the Future Ganqiong Li, Xin Li, Longhua Zhao, Shiwei Xu Journal of Agricultural Big Data    2020, 2 (3): 3-12.   DOI: 10.19788/j.issn.2096-6369.200301 Abstract （2445）   HTML （102）    PDF（pc） （1052KB）（2808）       Knowledge map    Save Agricultural supply chain is an indispensable part in the construction of modern agricultural market system in China. The establishment of a modern agricultural supply chain system is of great significance to promote the integrated development of primary, secondary and tertiary industries in rural areas, improve the modernization level of circulation, and enhance agricultural competitiveness and voice in the international market. Since entering WTO, China agriculture is highly open, and increasingly affected by the global agricultural supply chain in China. Strengthening agricultural supply chain management has become the focus of academic research, the difficulties of government concern and management control as well as the focus of agricultural producers and operators under the new situation of the global spread of COVID-19. Research of agricultural supply chain management started late in China, but it has developed rapidly in the past 10 years. Through literature research and related scientific research practice, this paper analyzes and compares the definitions of agricultural supply chain from different aspects, elaborates the concept and connotation of agricultural supply chain, and emphatically summarizes the research and application progress of agricultural supply chain management in theoretical innovation, mode innovation, risk management and technology innovation. Finally, combined with the new situation at home and abroad and the actual situation in China, the study puts forward the future research direction and hot areas that need to be further studied such as agricultural supply chain emergency management under emergencies, agricultural supply chain governance ability under the background of economic globalization, construction and management of green agricultural supply chain system, innovation of intelligent agricultural supply chain, and provide suggestions for promoting the development of agricultural supply chain management in China. Reference | Related Articles | Metrics | Comments（0）

Select

Sensitivity Analysis of Genetic Parameters of RiceGrow Model Yijun Meng, Xiaolei Qiu, Leilei Liu, Bing Liu, Yan Zhu, Weixing Cao, Liang Tang Journal of Agricultural Big Data    2021, 3 (3): 23-32.   DOI: 10.19788/j.issn.2096-6369.210303 Abstract （1668）   HTML （35）    PDF（pc） （1112KB）（2773）       Knowledge map    Save Genetic parameter calibration is an important step before applying the crop growth model, which often calls for a lot of time and effort. Sensitivity analysis can help to identify sensitive parameters, improve calibration efficiency, and simplify the model. Using Simlab and Matlab software, this study analyzed the sensitivity of rice genetic parameters of RiceGrow model by EFAST method and obtained the parameter sensitivity of the model in different regions and under different climate scenarios (historical meteorological data from 1981 to 2015 and global future warming 2.0℃ climate scenarios). The TDCC (Top-Downward-Coefficient of Concordance) coefficient was used to calculate the sensitivity ranking consistency. The results showed that Optimum Temperature (OT) was the most sensitive parameter affecting flowering period and total dry matter, followed by Temperature Sensitivity (TS), Photoperiod Sensitivity (PS) and Intrinsic Earliness (IE). OT was the most sensitive parameter affecting maturity period and the whole growth period. TS, IE, PS and Basic Filling Factor (BFF) were also sensitive parameters. The sensitive parameters affecting yield are mainly maximum CO2 assimilation rate (AMX), Specific Leaf Area (SLA) and Harvest Index (HI), followed by IE, TS, BFF, OT and PS. The sensitivity parameters in all regions and under different climate scenarios are relatively consistent, but the sensitivity ordering varies greatly. The sensitivity indexes of most parameters under warming climate scenarios slightly increase, while a few slightly decrease. The variation of parameter sensitivity under different climate scenarios is small, while which among different regions is large. When calibrating the model for phenology and dry matter, OT is the most sensitivity. In areas with low temperature and high latitude, the parameters related to temperature, photoperiod and photosynthesis should be focused. When calibrating the parameters of the yield, we need to focus on AMX, HI, SLA. Relative growth rate of LAI is not sensitive, so it can be ignored in parameter calibration, and can also be eliminated from the model to simplify the model. The results would be used to localize crop model and provide a way to improve the efficiency of parameter calibration. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （2581）   HTML （147）    PDF（pc） （1305KB）（2687）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1808）   HTML （74）    PDF（pc） （622KB）（2662）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1908）   HTML （70）    PDF（pc） （1013KB）（2654）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research Review on Personalized Text Retrieval in the Academic Scene ZHANG Jie, ZHU Liang, KOU YuanTao Journal of Agricultural Big Data    2023, 5 (4): 24-36.   DOI: 10.19788/j.issn.2096-6369.230403 Abstract （483）   HTML （20）    PDF（pc） （1000KB）（2643）       Knowledge map    Save This paper summarizes the research status of personalized academic text retrieval and provides reference and prospect for the follow-up research. We retrieved a total of 154 literature after screening and adding, used literature analysis method to summarize the research framework of personalized academic text retrieval, and discussed the core research and auxiliary research points in detail. Research on personalized academic text retrieval has been gradually systematic, moving from theoretical research to both theoretical and practical research. At present, there are some research problems, such as the low burden and high privacy interaction mode has not been realized, the deep personalized retrieval oriented to cognitive elements has not been realized, and the pre-research on appropriate context recognition is missing. The future development direction of personalized academic text retrieval is to actively embrace the ability endowed by new technologies such as large language model, and move towards cognitive-oriented, context-embedded and supporting real-time interaction. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （3018）   HTML （86）    PDF（pc） （687KB）（2579）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Statistical Dataset of Land Economic Survey in Jiangsu Province, China WANG ZiYu, JI YueQing, ZHOU Li Journal of Agricultural Big Data    2023, 5 (4): 138-143.   DOI: 10.19788/j.issn.2096-6369.230418 Abstract （2679）   HTML （236）    PDF（pc） （310KB）（2564）       Knowledge map    Save Effective land scale management is crucial for achieving agricultural modernization in China. However, the current landscape is plagued by practical challenges, including the sluggish growth of land transfer and the hindrance of moderate scale management. Jiangsu Province, characterized by its high economic development and a well-established land transfer market, has successfully addressed these issues through innovative approaches such as land readjustment and the establishment of intermediary organizations. The dataset is based on the 2020-2022 China Land Economic Survey (CLES) database, and it has been organized into three datasets comprising 943 plots, 5923 households, and 114 villages, following standardized procedures. The data content includes information on land use and transfer, as well as scale. The empirical evidence provided by this data set offers valuable insights into land transfer and large-scale operation in Jiangsu Province, thereby serving as a reference for government departments in formulating effective policy interventions. Data summary: Item Description Dataset name Statistical Dataset of Land Economic Survey in Jiangsu Province, China Specific subject area Agricultural economics Research topic Land transfer and large-scale management Time range 2019-2021 Geographical scope Jiangsu province Data types and technical formats *.xlsx Dataset structure The dataset consists of three XLSX files: (1) 943 land parcel dataset: including information on land basic characteristics, ownership and input-output (2) 5923 household dataset: including information on household land use and transfer (3) 114 village dataset: including information on village land use, transfer, and large-scale management Volume of data 2.75 MB Key index in dataset The cultivated land area under management, The rate of arable land transfer Data accessibility CSTR：17058.11.sciencedb.agriculture.00078 DOI：10.57760/sciencedb.agriculture.00078 Financial support Major Bidding Program of National Social Science Foundation of China "Research on the Path and Policy System for Achieving High-quality Development of Grain Industry in China" (21&ZD101) General Program of National Natural Science Foundation of China " The Lone Wolf Dies but the Pack Survives: Local External Economies for Smallholders’ Farm-size Choice in Modern Agriculture" (72073066). Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （2741）   HTML （90）    PDF（pc） （1192KB）（2524）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Aphid Image Dataset Based on Natural Background DONG Wei, ZHU JingBo, GUAN BoLun, KONG JuanJuan, LI RunMei, ZHANG Meng, ZHANG LiPing Journal of Agricultural Big Data    2023, 5 (3): 112-117.   DOI: 10.19788/j.issn.2096-6369.230315 Abstract （1389）   HTML （78）    PDF（pc） （2433KB）（2502）       Knowledge map    Save Agricultural pests are important reasons affecting crop yield and quality. Aphid is an important group of agricultural pest. Detecting and counting aphids is an important link for early detection and management of this pest. With the development of information technology, many experts and scholars have conducted extensive research on the identification of agricultural pests using computer vision, and have made certain progress. High-quality and large-scale basic data often play a decisive role in the development of computer vision, but the lack of this kind of image data is one of the challenges faced by pest identification. Aphids have features such as small size, dense distribution, inter insect shelter, and multiple forms of same species. These features also pose a serious challenge for the detection and counting of aphids. This article provides a total of 6287 high-definition original images, including a dataset of 13 agricultural pests (aphids) including peach aphid, cotton aphid, and grain constrictor aphid, etc. These aphid images were collected using DSLR cameras in a natural field environment. In order to ensure the high quality and reliability of the data, these images are cleaned and organized by professional personnel, and identified and classified by experts in the field of plant protection. This dataset can provide a data foundation for recognition, detection, counting and classification of aphids. Data summary: Items Description Dataset name Aphid Image Dataset Based on Natural Background Specific subject area Plant protection Research topic Aphid Time range 2013-2023 Geographical scope China Data types and technical formats Data type: image; Technical formats:*.jpg Dataset structure The dataset contains a total of 6287 images of 13 types of aphids, including Hyalopterus amygdali, Myzus persicae, Aphis gossypii, Rhopalosiphum padi, Aphis spiraecola, Aphis craccivora, Uroleucon formosanum, Sitobion miscanthi, Brevicoryne brassicae, Lipaphis erysimi, Rhopalosiphum maidis, Panaphis juglandis, and Nippolachnus piri. Volume of data 16.8 GB Data accessibility CSTR: https://cstr.cn/17058.11.sciencedb.agriculture.00030 DOI: https://doi.org/10.57760/sciencedb.agriculture.00030 Financial support General Program of National Natural Science Foundation of China “Research on Few-shot Pest Recognition Inspired by Knowledge Transfer and Causal Reasoning”（32171888） Anhui Academy of Agricultural Sciences Research Platform Project “Agricultural Intelligent Technology Research and Development Center”（2023YL1014） Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （1487）   HTML （50）    PDF（pc） （586KB）（2404）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Construction of a Visual Analysis Platform for Microorganism Resources Big Data from Cotton Fields in Xinjiang, China Haiyan Liu, Rong Yang, Tongyu Hou, Wei Zhao, Zhaoqun Yao, Haijiang Wang, Ze Zhang, Pan Gao, Lü Xin Journal of Agricultural Big Data    2021, 3 (1): 45-55.   DOI: 10.19788/j.issn.2096-6369.210105 Abstract （1107）   HTML （22）    PDF（pc） （1735KB）（2398）       Knowledge map    Save The effective integration and scientific analyses of basic information between the big data of Xinjiang cotton field soil microbial resources and the multiple heterogeneous agricultural resources data were performed. In accordance with cotton planting areas in different regions and of different maturity levels in Xinjiang, as determined using the agricultural big data platform of Xinjiang Production Corp., the microbial group database and big data visualization analysis process of a typical cotton field ecosystem in China were established. Using LEfse and an RDA analysis, the soil microbial diversity and community structure in Xinjiang cotton fields from 2017 to 2019 were analyzed, and the effective integration of cotton field soil microbial resources and multiple heterogeneous agricultural resources data was realized by modeling. The Xinjiang cotton field soil microbial resources database and the visualization analysis of soil microbial diversity, which provided approximately 1.7 GB of soil microbial information and 5–6 GB of environmental information, were preliminarily established. Using the platform, soil bacterial community structures in the Exceptional Early Maturing Cotton Areas (Bole, Shihezi, and Fukang), Early Maturing Cotton Area (Kuitun), and Early–Middle Maturing Cotton Area (Hami) were found to have changed greatly at the phylum level, with Proteobacteria accounting for 20.9% to 29.8%, Acidobacteria accounting for 16.1% to 30.6%, Verrucomicrobia accounting for 7.0% to 28.9%, and Chloroflexi accounting for 6.6% to 21.2%. The LEfse revealed that there were 255 different species, with Sphingomonasdales, Desulfobacterales, and Geobacter being the main species having different microbial community structures in Northern Xinjiang. The collection, management, and analysis of soil microbial diversity data in the Xinjiang cotton area plays an important supporting role in the construction and application of a big data platform for cotton production and agriculture from the Xinjiang Production Corp., which will lay a scientific foundation for the protection and utilization of soil microbial diversity resources in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Development and Application of Digital Control Platform in Large- scale Beef Cattle Farm——Take the 5G digital ranch of Yangxin Yi Liyuan Halal Meat Co., Ltd as an example ZHANG Fan, ZHOU MengTing, LIU MinZe, TANG XiangFang, XIONG BenHai Journal of Agricultural Big Data    2024, 6 (1): 68-81.   DOI: 10.19788/j.issn.2096-6369.000009 Abstract （797）   HTML （20）    PDF（pc） （5163KB）（2361）       Knowledge map    Save As large-scale beef cattle facilities become more and more common in China, sophisticated and even intelligent management of cattle has gained prominence. In the meantime, the Internet of Things, big data, artificial intelligence (AI) and even large models are developing at a rapid pace and are constantly permeating in all industries, making the intelligent management, including traditional breeding, possible. In this study, Yangxin Yi Liyuan 5G digital farm was used as the research object, which integrated application of intelligent electronic ear tags and intelligent collars, as well as a variety of environmental sensors for temperature and humidity, ammonia, carbon dioxide, wind direction and speed, light and air quality (H2S, PM2.5, PM10, TSP). Simultaneously, a comprehensive dynamic perception of individual physiological indicators, such as the degree of exercise and rumination, was utilized to determine the estrus period of breeding cattle and forecast the ideal breeding period, and was employed to ascertain the estrus period of breeding cattle, forecast the ideal mating period, and determine whether the feed adjustment was necessary by monitoring variations in rumination time. Comprehensively monitoring the environmental indicators, such as temperature, humidity and air quality, is necessary to accurately manage and ventilate the cattle house. The pertinent data was analyzed using the MY SQL database technology and DELPHI language technology. In order to create a digital control platform for the breeding environment, health status, epidemic prevention, and feed management of beef cattle, this research built a digital control platform that integrated automatic collection of key data in breeding links, automatic conversion and calculation of data, wired and wireless transmission of data, remote storage and processing control of data, and so on. This study also shown that the more the system was used and improved, the more data was collected and extended in both size and type during the breeding process, and the more fundamental and derived data could be mined and appreciated. This paved way for the eventual development of a large-scale model for breeding beef cattle in the future. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Analysis and Application of High-throughput Plant Phenotypic Big Data Collected from Unmanned Aerial Vehicles Peisen Yuan, Mingjia Xue, Yingjun Xiong, Zhaoyu Zhai, Huanliang Xu Journal of Agricultural Big Data    2021, 3 (3): 62-75.   DOI: 10.19788/j.issn.2096-6369.210307 Abstract （3502）   HTML （119）    PDF（pc） （1209KB）（2270）       Knowledge map    Save Plant phenotypes refer to the physical, physiological and biochemical characteristics and traits that are determined or influenced by genes and environmental factors. Accurate and rapid access to plant phenotypic information under different environmental conditions, and the analysis of the genetic and performance patterns of their genomes, can effectively promote research on the correlation between genomic and phenotypic information. The Unmanned Aerial Vehicle (UAV) high-throughput plant phenotyping platform is suitable for acquiring plant phenotypic data in field environments owing to the UAV’s mobility and flexibility, and it has the great advantages of a high data acquisition efficiency and low cost. With the help of advanced sensor technologies, such as hyperspectral imaging and LIDAR, the UAV provides a feasible way to efficiently acquire plant phenotypic data. Effective analyses and processing methods and techniques for plant phenotypic data acquired by UAVs must be employed. Thus, high-throughput plant phenotypic analyses based on UAV platforms provides an important tool for studying plant phenotypic information from the field. This paper summarizes and analyzes the latest research results of UAV-based high-throughput crop phenotyping using big data analysis technology and artificial intelligence, as well as its research principles, relevant algorithms, processes, key technologies and applications. The main focus is on big data processing and intelligent analysis techniques related to UAV-based high-throughput plant phenotype big data and to the analysis of typical phenotypes, such as plant height, leaf area index, and plant diseases. We analyzed the current research needs and provide both a summary and outlook on related applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research Progress on Fruit Tree Germplasm and Breeding of New Varieties in Northern China Yuan Gao, Dajiang Wang, Simiao Sun, Lin Wang, Kun Wang, Yufen Cao, Peihua Cong, Caixia Zhang, Haibo Wang Journal of Agricultural Big Data    2022, 4 (2): 5-12.   DOI: 10.19788/j.issn.2096-6369.220201 Abstract （1000）   HTML （20）    PDF（pc） （601KB）（2197）       Knowledge map    Save Fruit trees in northern China are mainly deciduous fruit trees such as apple, pear, peach and grape, and the four major tree species are the important sources of fruits in China. Fruit tree germplasm resources are the "chip" of fruit tree seed industry and the material guarantee for the development of fruit industry. The Research Institute of Pomology of Chinese Academy of Agricultural Sciences was officially established 64 years ago, who is the first scientific research institution to carry out relevant research in China, but research on fruit tree germplasm resources and new variety breeding of history has been engaged in for more than 70 years. It has established the National Repository of Apple and Pear Germplasm Resources in Xingcheng, which has the longest history and ranks in the world in the number of apple and pear germplasm resources, and won the second prize of the National Science and Technology Progress Award. It has bred more than 100 new varieties and dwarf rootstocks of apples, pears, grapes and peaches, and published many influential books. Our efforts and achievements have provided a strong scientific and technological support for the rapid development of China's fruit industry, and had a profound impact on the sustainable development of China's apple, pear, peach, grape and other industries. This article reviewed and summarized the main achievements in extensive collection, proper preservation, in-depth research, active innovation, sharing and utilization and breeding of new varieties and so on. And finally from the following four aspects: improving the protection system of fruit tree germplasm resources combined with multiple conservation methods, identifying of fruit tree germplasm resources under the guidance of national and industrial development needs, mining fruit tree germplasm resources with refined evaluation and accurate positioning, and promoting new varieties through innovation of breeding technologies, we look forward to the main research direction and content of our institute in the future in the high-level protection and high-quality utilization of fruit tree germplasm resources, We will continue to make greater contributions to the building of a powerful fruit industry and rural revitalization. Reference | Related Articles | Metrics | Comments（0）

Select

Application and Prospects for Big Data of Traditional Chinese Medicine Resources Mingxu Zhang, Yuan Chen, Tuya Xilin, Ru Zhang, Yaqiong Bi, Chunhong Zhang, Taotao Wu, Minhui Li Journal of Agricultural Big Data    2021, 3 (1): 14-24.   DOI: 10.19788/j.issn.2096-6369.210102 Abstract （1992）   HTML （38）    PDF（pc） （1523KB）（2182）       Knowledge map    Save Big data refers to the collection of massive amounts of information, and it has four characteristics: large amounts of data; strong real-time performance; multiple types of data; and valuable data. Following the rapid development of computer science and information technology, big data has been widely applied in the field of health and medicine. China has undergone major improvement to its national big data resources. In conjunction with that process and the promotion of the Belt and Road Initiative, the need to upgrade and transform China’s traditional medicine resource industry has clearly emerged. Integrating China’s traditional medicine resource industry with big data analysis technology can effectively promote the development of industries related to such medicine as well as in-depth research. The comprehensive development of big data of traditional Chinese medicine resources has increasingly become an important driving force in developing related industries. With respect to traditional Chinese medicine resources, big data mainly involves the accumulation and processing of large volumes of information, such as the following: the number of types of traditional Chinese medicine resources; the spatial distribution of plant and animal species used in traditional Chinese medicine; the number of industrial resources; changes in the availability of resources; cultivating plant and animal species for traditional Chinese medicine versus harvesting them from the wild; the amount of resources that need to be purchased; demand level; supply level; the quality of materials used in traditional Chinese medicine; and knowledge related to the application of such medicine. Analyzing those data can exert a vital influence on overall planning related to the resource industry for traditional Chinese medicine. Accordingly, an opportune assessment of the application and development of big data technology for Chinese medicine resources can better guide the direction of future research. The present study begins with an application of big data technology to traditional Chinese medicine resources. This paper summarizes the current status regarding the development of resource databases for traditional Chinese medicine. This study made use of dynamic monitoring stations for traditional Chinese medicine resources; it also describes how research has progressed with respect to the application of big data technology for resources for traditional Chinese medicine. This study concludes with suggestions for problems that may occur in developing big data technology for Chinese medicine resources. This article covers scientific planning and offers guidance for the sustainable development of the resources industry for Chinese medicine; it sets a foundation for additional high-quality research into clinical treatment for traditional Chinese medicine as well as the pursuit of careers in such medicine. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

The Development of Blockchain and Its Application in Agriculture Huijuan Wang, Qian Xu, Ailian Zhou, Xiaohe Liang, Nengfu Xie, Xiaoyu Li, Saisai Wu Journal of Agricultural Big Data    2021, 3 (3): 76-86.   DOI: 10.19788/j.issn.2096-6369.210308 Abstract （1329）   HTML （41）    PDF（pc） （839KB）（2148）       Knowledge map    Save [Related Concepts]Blockchain is a decentralized, open and shared distributed database that has the characteristics of decentralization, high openness, anonymity, machine autonomy, anti-tampering, and traceability. [Current Research Status]At present, blockchain has become a leading technology to advance the development of countries worldwide and has been applied in many fields, including finance, education, and medical care. Its applications are continuously emerging, and China has now included it as part of the national technology strategy. Most of the available research literature on blockchain technology focuses on its development and application in the financial sector. Few studies have attempted a systematic review of the development process and characteristics of blockchain technology, and "blockchain + agriculture" is still in its infancy. [Summary of This Paper]This paper systematically combs the main development process of blockchain, divides it into four stages (origin of the technology, blockchain 1.0, blockchain 2.0, and blockchain 3.0), summarizes the main characteristics and development status of each stage, and discusses in depth representative cases of blockchain application in the agricultural sector. By combing the development process and analyzing the application cases, this paper explores the application potential of blockchain technology, summarizes new opportunities for blockchain in agriculture, and puts forward ideas for using blockchain technology in agriculture in China. [Prospect]The application of blockchain technology can effectively reduce information asymmetry, improve information transparency, reduce data storage costs, and contribute to the development of modern agriculture. To make blockchain technology more applicable to agriculture, we need to understand the problems and challenges that may arise in the future, while still devising targeted measures to promote the development of blockchain for the agricultural sector. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Image Dataset of Stored Grain Pests by Henan University of Technology YU JunWei, ZHAI FuPin Journal of Agricultural Big Data    2023, 5 (2): 85-90.   DOI: 10.19788/j.issn.2096-6369.230213 Abstract （1403）   HTML （134）    PDF（pc） （903KB）（2050）       Knowledge map    Save As grain pests cause a major post-harvest loss in stored grains, early detection and monitoring of grain pest activities become necessary for applying appropriate actions to reduce storage losses. With the development of artificial intelligence, image detection methods based on deep learning have been widely used in agriculture. However, current research in stored grain pest detection is relatively limited. The quality of the dataset will determine the level of knowledge that deep learning models can learn. Therefore, constructing a specialized dataset for grain pest detection and counting is of great significance. The proposed dataset GrainPest includes 500 original images of grain insects, 500 pixel-level saliency annotation images, 420 files with insect bounding boxes and 500 entries of pest counts. The data set covers various grain pests such as corn weevil, wheat moth, grain beetle, and corn borer, as well as different types of grain backgrounds such as wheat, corn, and rice. Due to the fact that many grains are not infected with pests, the GrainPest also includes 80 pure grain background images without any pest, which bring more challenge for saliency detection. The GrainPest provides a benchmark dataset to promote the research of saliency detection, object detection, and pests counting in stored grains, and the work will provide support for reducing grain storage losses and ensuring food security. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Progress of Agricultural Big Data Research (2024) Agricultural Information Institute of CAAS Journal of Agricultural Big Data    2024, 6 (4): 433-468.   DOI: 10.19788/j.issn.2096-6369.200003 Abstract （681）   HTML （85）    PDF（pc） （10122KB）（2034）       Knowledge map    Save Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Quality Index Data Set of 12 Apple Varieties from Different Habitats Chaoshuang Jia, Zhihua Wang Journal of Agricultural Big Data    2022, 4 (2): 20-24.   DOI: 10.19788/j.issn.2096-6369.220203 Abstract （1780）   HTML （73）    PDF（pc） （475KB）（2001）       Knowledge map    Save China is a large country of apple production, with many varieties, and the fruit quality characteristics of different apple varieties are very different. The data of apple quality indicators of different varieties are measured and collected to provide a theoretical basis for apple variety breeding, processing and utilization. The data set collected 12 apple varieties from different production areas (Shandong, Liaoning, Hebei, Shanxi), including ‘Jonagin’, ‘Jinguan’, ‘Huahong’, ‘Hanfu’, ‘Guoguang’, ‘Dounan’, ‘Huayue’, ‘Ruixianghong’, ‘Ruiyang’, ‘Ruixue’, ‘Huaqing’, ‘Venus gold’, and sorted out the fruit quality indicators of 12 apple varieties from 2019 to 2021, including hardness, soluble solids, titratable acid, vitamin C, respiratory intensity, ethylene release, bursting power Rupture displacement, rupture work, yield stress, yield work, yield displacement, pulp hardness, pulp work, L* value, a* value, b* value, c* value, h* value, 19 quality indexes in total. The data set collected this time covers the physiological quality, pulp texture, color and other aspects of the fruit, more comprehensively reflects the characteristics of the variety, and the evaluation of the variety can be more authentic and accurate. The establishment and sharing of data sets for quality indicators of different apple varieties can enable the public to more clearly understand the differences in traits among apple varieties, carry out targeted breeding and processing, and screen out varieties with outstanding traits to select regions more suitable for sales. Table and Figures | Reference | Related Articles | Metrics | Comments（0）