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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 （1624）   HTML （229）    PDF（pc） （4768KB）（2354）       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）

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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 （780）   HTML （32）    PDF（pc） （802KB）（896）       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）

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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 （247）   HTML （22）    PDF（pc） （911KB）（685）       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）

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A 10 m Spatial Resolution Dataset for the Spatial Distribution of Cropland Resources in the Three Northeastern Provinces from 2020 to 2022 SHEN Ge, LIU Hang, LI DanDan, CHEN Shi, ZOU JinQiu Journal of Agricultural Big Data    2023, 5 (2): 2-8.   DOI: 10.19788/j.issn.2096-6369.230202 Abstract （211）   HTML （37）    PDF（pc） （1074KB）（532）       Knowledge map    Save Timely and accurate acquisition of cropland spatial distribution data is of great significance for agricultural production management, planting structure adjustment and food security. In this study, three northeastern provinces (Heilongjiang, Liaoning and Jilin) were selected as the research area. Based on massive Sentinel-2 data, a 12-month Normalized Difference Vegetation Index (NDVI) dataset of cropland and non-cropland samples in a specific year was established in each province. The Google Earth Engine remote sensing computing platform was used to conduct supervised classification according to the difference characteristics of NDVI between cropland and non-cropland samples, and the spatial distribution data set of cropland resources with 10 m spatial resolution in the three northeastern provinces during 2020-2022 was obtained. The data set is an update of the latest available cropland resource data set, which can provide data support and scientific services for the scientific protection of phaeozem in Northeast China, the implementation of the strategy of "storing grain in land, storing grain in technology" and the guarantee of food security. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （943）   HTML （44）    PDF（pc） （475KB）（463）       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）

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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 （232）   HTML （40）    PDF（pc） （903KB）（412）       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）

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A Dataset of National Agricultural Machinery Purchase and Application Subsidy Information from 2021 to 2022 YANG WenJun, GU XiangQun, YANG ZhiHai Journal of Agricultural Big Data    2023, 5 (3): 49-55.   DOI: 10.19788/j.issn.2096-6369.230309 Abstract （190）   HTML （19）    PDF（pc） （326KB）（400）       Knowledge map    Save As an important policy to strengthen and benefit agriculture, the agricultural machinery purchase subsidy policy has a profound impact on many parties, such as new agricultural business entities, agricultural production, and the agricultural machinery industry. Through the collection of national agricultural machinery purchase and subsidy data, we can grasp the differences and trends in the purchase of agricultural machinery in various regions. We can then reasonably assess the impact of the policy, adjust the policy objectives and content, promote the healthy development of the agricultural machinery market, and better assist the construction of a strong agricultural country. At present, the Department of Agriculture and Rural Affairs of each province in China, through the information disclosure column of agricultural machinery purchase subsidies, releases data related to agricultural machinery purchase in real time. Through network crawling and processing, this dataset covers the subsidy data on the purchase and application of agricultural machinery in 23 provinces (autonomous regions and municipalities directly under the central government) such as Beijing, Tianjin, Shanxi, etc. in 2021-2022, totaling 222,6229 items. This dataset can be used to analyze the characteristics and differences of the purchase of agricultural machinery and the subsidy distribution in different regions, and provide a data basis for related scientific research and management decision-making. Data summary: Items Description Dataset name A Dataset of National Agricultural Machinery Purchase and Application Subsidy Information from 2021 to 2022 Specific subject area Agriculture economics Research topic Subsidies for the purchase of agricultural machinery Time range 2021-2022 Geographical scope Beijing, Tianjin, Shanxi and other 23 provinces (autonomous regions and municipalities directly under the central government) Data types and technical formats Preprocessed data (EXCEL format) Dataset structure It is divided into 3 files by province. The first one is Chongqing, Shanxi, Zhejiang, Shaanxi 2021-2022 dataset. The second is Henan, Hubei, Jiangxi, Xinjiang, Heilongjiang, Xizang 2021-2022 dataset. The third is Hebei, Gansu, Anhui, Fujian, Guangxi, Liaoning, Guizhou, Hainan, Ningxia, Qinghai, Tianjin, Beijing, Shanghai 2021-2022 dataset Volume of data 259.49 MB Key index in dataset 19 indicators, including province, county, township (town), corresponding character of the purchaser's name, purchaser's name, and item of the machin Data accessibility CSTR：31253.11.sciencedb.12793 DOI：10.57760/sciencedb.12793 Financial support Ministry of Education, Philosophy and Social Science Major Project (No. 20JZD015); National Natural Science Foundation of China (No. 72303076); National Innovation and Entrepreneurship Training Programme for College Students (No. 202310504074) Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （142）   HTML （20）    PDF（pc） （4374KB）（389）       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）

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Near-infrared Spectral Data Set of Apple Quality and Disease of Fuji Apple in Liaoning Province Xianghe Zhang, Tingting Liu, Xiaoli Wang, Lin Hu, Jingchao Fan, Linlin Jiang Journal of Agricultural Big Data    2022, 4 (2): 30-35.   DOI: 10.19788/j.issn.2096-6369.220205 Abstract （474）   HTML （33）    PDF（pc） （628KB）（375）       Knowledge map    Save In recent years, near infrared spectroscopy (NIRS) has become the preferred method for rapid nondestructive testing due to its effectiveness. At present, the collection of near-infrared spectral data of apple fruits is limited to the internal quality of apples. However, apple fruit is prone to disease, and its disease spectral data is of great significance, which has the value of redevelopment and utilization, and is helpful for scientific research and discovery. In this data set, the Fuji apples produced in Xingcheng city of Liaoning Province in 2010 were inoculated with anthrax and Physalospora piricola, and the relevant data were collected by NIR spectroscopy. Meanwhile, the NIR data of the original quality was collected as the control. This data set includes 2150 pieces of near-infrared spectral detection data of apple quality and 958 pieces of disease detection data. The data storage format is ASD file, totaling 121MB. The data set can provide a data basis for the identification model of apple fruit diseases and the research of nondestructive testing methods. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research and Analysis of Typical Databases in Major Frontier Fields at Domestic and International Level DUAN Bowen, WANG Juanle, SHI Lei, GAO Mengxu Journal of Agricultural Big Data    2023, 5 (1): 46-54.   DOI: 10.19788/j.issn.2096-6369.230113 Abstract （171）   HTML （11）    PDF（pc） （1069KB）（359）       Knowledge map    Save Science data is the basis of the innovation value chain "data-information-knowledge-wisdom", and is the most basic science and technology resource, which plays an important role in economic and social development and scientific innovation. “Outline of the 14th Five-Year Plan (2021—2025) for National Economic and Social Development and Vision 2035 of the People's Republic of China”deployed nine frontier areas for the implementation strategic science programs and science projects. A timely grasp of the current situation and demand for science data sharing in these frontier areas was significant for better strengthen the construction of China's Science Data Center and to play the role of data support for the frontier areas. This paper tracked the domestic and foreign progress in nine areas databases including artificial intelligence, quantum information, integrated circuits, life and health, brain science, biological breeding, deep earth, ocean science, and sustainable development, and investigated and analyzed from data resources, database/platform integration capabilities, application services and typical cases. The study took PANGAEA database as a representative case, which in German and in the deep sea and earth system science field, analyzed its characteristics in organizational structure, technical operation and maintenance, and operation and management process. Suggestions for scientific data governance were proposed for the requirements of frontier fields development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （227）   HTML （21）    PDF（pc） （359KB）（359）       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）

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Exploration and Application of Big Data Technology in Pork Price Prediction and Regulation JIN Yuze, JIA Xinwei, LAI Wangfeng, ZHOU Hongli, CHEN Naihe, LI Tao Journal of Agricultural Big Data    2023, 5 (1): 126-134.   DOI: 10.19788/j.issn.2096-6369.230121 Abstract （733）   HTML （42）    PDF（pc） （438KB）（334）       Knowledge map    Save China is a country of large hog production and pork consumption. Fluctuations in pork prices directly affect the interests of hog farmers and residents' diets. Prediction of the future trend of pork prices and scientific control of pork prices plays an important and practical role in promoting the stable and healthy operation of hogs and pork industry of China. This article studies the national pork market price trend. Firstly, pork supply prediction model based on the number of live hog and breeding sows is built according to the biological cycle and continuity features of hog production, which can predict pork production in the next 10 months. Secondly, taking advantage of the obvious seasonal cycle fluctuations in pork demand caused by my country's pork consumption habits, the STL time series decomposition method is used to decompose the monthly seasonal fluctuation trend from the pork transaction data to predict the monthly pork demand. Thirdly, based on the law of supply and demand in the pricing model, the relationship model of the pork price and the ratio between pork supply and demand is constructed to predict pork price in the next 10 months and calculate the price of pork supply and demand equilibrium. The relative error of pork price prediction is about 10% by using pork-related data from the Agricultural Products Market Information Platform system of the Ministry of Agriculture and Rural Affairs in 2022. When the estimated future pork supply and demand deviates, the model can adjust the pork supply by regulating the number of breeding sows, the import volume and the delivery volume, thereby regulating the future pork price trend. This study provides ideas and methods to adjust future pork price trends by adjusting the core factors that affect pork supply. This study aims to assist relevant government departments in properly and timely regulation of pork supply on the basis of scientifically predicting pork supply and demand and future price trends, so as to balance the supply and demand of pork and maintain the price of pork within a reasonable range of balanced supply and demand. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress on Breeding Techniques of Main Fruit Trees in Northern China Fei Wang, Chunqing Ou, Yanjie Zhang, Haibo Wang, Li Ma, Shuling Jiang Journal of Agricultural Big Data    2022, 4 (3): 6-14.   DOI: 10.19788/j.issn.2096-6369.220301 Abstract （317）   HTML （17）    PDF（pc） （635KB）（333）       Knowledge map    Save Breeding technology is an important tool for fruit tree breeding. Fruit tree breeding techniques mainly include conventional breeding techniques such as cross breeding, mutation and seedling selection, and auxiliary breeding techniques including radiation breeding, molecular marker assisted breeding, and genetic transformation. At present, conventional breeding technology is the main technology for breeding major fruit trees in northern China, and most varieties are selected through conventional breeding techniques. Mutation breeding and tissue culture are important auxiliary breeding techniques that play valuable roles in fruit breeding in northern China. Molecular marker-assisted breeding and genetic transformation technologies were used, molecular markers related to important traits were screened and developed, and important transgenic fruit trees were obtained, which laid an important foundation for molecular breeding of fruit trees. Using apple, pear, grape, and peach as examples, this paper summarized the breeding techniques of major fruit trees in northern China, introduced the characteristics and application of breeding techniques, and addressed the future of fruit tree breeding strategies and methods to provide ideas and references for the breeding major fruit trees in northern China. Reference | Related Articles | Metrics | Comments（0）

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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 （399）   HTML （14）    PDF（pc） （601KB）（332）       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）

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A Dataset on Spatial Distribution of Mangroves in Qi'ao Island, Zhuhai, Guangdong Province from 2000 to 2020 CAI HuiNa, WANG Ruei-Yuan Journal of Agricultural Big Data    2023, 5 (2): 9-15.   DOI: 10.19788/j.issn.2096-6369.230203 Abstract （188）   HTML （33）    PDF（pc） （3149KB）（321）       Knowledge map    Save Mangroves are distributed in the tropical and subtropical coastal intertidal zones, playing a critical role in the global carbon cycle and providing service value for ecological and ecological economic development. But with the development of human activities and the deterioration of the natural environment, mangrove resources have sharply decreased. Using Landsat images of 2000, 2005, and 2010, and Sentinel-2 images of 2015 and 2020, through decision tree classification and object-oriented classification methods, combined with field surveys, the study extracts the spatial distribution and area of mangroves each year, and produces the spatial distribution dataset of mangroves in Qi'ao Island from 2000 to 2020. The article dataset can analyze the spatial dynamic evolution of mangroves, providing important references for scientific research such as the dynamic changes of mangroves on Qi'ao Island and the evaluation of ecological environment quality; providing decision-making support for the protection, restoration, and management of mangrove wetlands; and providing basic data support for environmental monitoring in Zhuhai and Guangdong Province. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Journal of Agricultural Big Data    2023, 5 (2): 1-1.   DOI: 10.19788/j.issn.2096-6369.230201 Abstract （103）   HTML （26）    PDF（pc） （237KB）（317）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Developing Scientific Data Fundamental Institution for Stimulating Its Innovation Energy: On ‘Data 20 Article’ and Establishment of National Data Bureau ZHOU Yuanchun, CHEN Xin Journal of Agricultural Big Data    2023, 5 (1): 11-14.   DOI: 10.19788/j.issn.2096-6369.230105 Abstract （128）   HTML （16）    PDF（pc） （1768KB）（311）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Creation and Application of an Intelligent Pig Farm Digital Control Platform Liang Yang, Huajie Gao, Alin Xia, Benhai Xiong Journal of Agricultural Big Data    2022, 4 (3): 135-146.   DOI: 10.19788/j.issn.2096-6369.220321 Abstract （461）   HTML （18）    PDF（pc） （1683KB）（305）       Knowledge map    Save China is a major pig breeding country worldwide. Creating a digital control platform for intelligent pig farms is inevitably needed for the development of large-scale pig farms and is of great significance for promoting the transformation, upgrading, and healthy development of China’s pig industry. However, the low levels of digitalization, intelligence, and related technology seen in Chinese pig farms today severely limit their ability to expand further in the face of an outbreak of African swine fever and a food scarcity. Therefore, the use of modern information technology to construct an intelligent pig farm digital management and control platform will play a positive role in addressing the issues that Chinese pig farms face, such as environmental pressure, resource constraints, breeding losses, and other practical problems. Thus, it will promote the development of China’s pig industry. In this study, we created an intelligent pig farm digital control platform, system integration Internet of Things artificial intelligence, and other modern information technology to build a pig multi-dimensional early warning system, intelligent management system, and production management decision system. This platform can ensure the safety of farm biosecurity and assets, record the whole production cycle of pigs from birth to market, refine production, and promote management visualization decision-making to achieve intelligent pig breeding and effectively improve pig farming income. This study also revealed that various aspects of this intelligent pig farm digital control platform required further improvement to enhance pig farm management in China, such as developing intelligent precise feeding, health status sensing, and big data analysis systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （177）   HTML （22）    PDF（pc） （3734KB）（288）       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）

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Quality Datasets of ‘Fuji’ Apples Cultivated in China in 2017 Lixue Kuang, Guofeng Xu, Zhijun Chen, Yinping Li, Yang Cheng, Youming Shen Journal of Agricultural Big Data    2022, 4 (3): 49-53.   DOI: 10.19788/j.issn.2096-6369.220307 Abstract （221）   HTML （7）    PDF（pc） （464KB）（268）       Knowledge map    Save Apple is an important part of Chinese diet. Apple quality is increasingly concerned and the market, especially the high-end market for apple quality requirements are increasing day by day. One hundred and seventy six ‘Fuji’ apple samples were collected from orchards of 8 main apple producing provinces, including Hebei, Henan, Liaoning, Shandong, Shanxi, Shaanxi, Ningxia and Xinjiang. Fifteen indexes were tested for each sample, including single fruit weight, fruit firmness, total soluble solid, soluble sugar, titratable acidity, vitamin C, sorbitol, glucose, fructose, sucrose, sweetness value, the ratio of total soluble solid to titratable acidity, the ratio of soluble sugar to titratable acidity, a the ratio of sweetness value to titratable acidity. The aim of this study was to establish the quality basic database of ‘Fuji’ apple. Through the establishment of ‘Fuji’ apple quality dataset, the nutritional quality of ‘Fuji’ apples from different production areas in China can be systematically mastered, the differences of ‘Fuji’ apples from different production areas can be explored, and the influencing factors can be explored. This study also provided theory basis for apple industry development and quality evaluation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Data Security on The Development of Digital Village Ruoxuan Wei Journal of Agricultural Big Data    2022, 4 (3): 109-115.   DOI: 10.19788/j.issn.2096-6369.220318 Abstract （493）   HTML （22）    PDF（pc） （632KB）（267）       Knowledge map    Save Digital village is an important measure to achieve rural revitalization and the modernization of agriculture and rural areas. However, in the process of digital rural development, a large number of important data related to agricultural production, business, scientific research and management are faced with a complex and severe data security situation. We must coordinate the development and security, and strengthen the management and technical protection of data security, and construct the line of data security in agricultural and rural areas. By comprehensively analyzing the risks of data confidentiality, integrity and availability faced by the construction of smart agriculture, the digital governance of rural society and the digital construction of agriculture sector in the digital village, the paper proposes to balance the relationship between data value mining and security protection, and build a comprehensive governance system for data security from the aspects of top-level design, the security of terminal facilities, network security, application security, the technical protection system and safeguards. By establishing a long-term governance mechanism, improving institutional norms, ensuring the security of agricultural terminal facilities, enhancing the capability of active defense, dynamic defense and precise defense to ensure network security and reliability, strengthening security protection of information systems related to production control, business management and government affairsm, establishing a technical protection system covering the data lifecycle, and implementing safeguard measures from data security risk assessment, supervision and talent training, the development of digital villages is escorted. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design and Implementation of Ministry Agriculture and Rural Affairs Network Security Situation Awareness Monitoring and Analysis Platform HU Yajie Journal of Agricultural Big Data    2023, 5 (1): 68-75.   DOI: 10.19788/j.issn.2096-6369.230115 Abstract （177）   HTML （10）    PDF（pc） （3269KB）（245）       Knowledge map    Save In order to protect network security, eliminate potential risks, ensure the safe and stable operation of network infrastructure and information systems, this paper aims to build a network security situation awareness monitoring and analysis platform for the Ministry of Agriculture and Rural Affairs, to realize network security situation awareness, traffic anomaly monitoring, incident safety warning, attack tracking, panoramic visual display, effectively responding various network security threats and challenges. The platform relies on big data technology and machine learning algorithms to conduct global network security situation assessment, eliminate threat anomalies, and handle attack events, thereby improving network security protection capabilities, it has achieved standardization of multi-source heterogeneous network security data, network server logs, traffic data of key nodes, management data, implemented global network security situational awareness that integrates network intrusion, horizontal threats, attacker tracing, asset threats, and application security, realized the visualization display of the entire process of security defense, including network security status, attack monitoring and disposal, realized the integration of network security defense, effectively ensuring the normal operation of business systems, effectively preventing destructive activities caused by viruses and Trojans, greatly improving the ability to quickly detect and respond to major network security incidents, and providing efficient protection measures for network security protection. Through the construction of the network security situational awareness monitoring and analysis platform of the Ministry of Agriculture and Rural Affairs, an effective path that can be replicated and promoted for network security data governance and integrated security monitoring and defense has been explored, and its construction ideas provide practical reference for provincial agricultural and rural departments. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （264）   HTML （30）    PDF（pc） （362KB）（243）       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）

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A Dataset of Coccinellids in Yunnan Red Pear, etc Crops, Chuxiong Prefecture, Yunnan Province from 2018 to 2021 Wenming Zhang, Chuantao Huang, Yonghai Sun, Kaixiong Hou, Min Lu, Wenjing Wang, Congzhi Fei Journal of Agricultural Big Data    2022, 4 (4): 87-93.   DOI: 10.19788/j.issn.2096-6369.220412 Abstract （261）   HTML （9）    PDF（pc） （1534KB）（241）       Knowledge map    Save Yunan is located in a low-latitude plateau. With extreme high biodiversity level, Yunnan is often considered as a specimen library for the world. Chuxiong is located in the hinterland of central Yunnan, where is the biological convergence zone between the southern hot area and the northwestern frigid area. It has unique climate conditions, including seven agricultural climate types from the cold temperate zone to the tropical zone. It has rich agricultural resources, high biodiversity level and abundant resources of potential natural enemies. Coccinellids are important natural enemy resources in Yunan, with great potential in pest biological control. There are few investigations on natural enemy resources such as coccinellids in farmland in Chuxiong. In this study, the occurrence of coccinellids in the local red pear orchard was monitored for four consecutive years from 2018 to 2021, with habitat and climatic conditions also recorded. A total of 16 genera with at least 30 Coccinellidae species were recorded and identified, with manual survey and insect collection using Malaise trap. through the method of insect collection and manual investigation in Malaise trap. This dataset provides basic data on local coccinellid resources, which is a basis for analyzing coccinellid diversity, population dynamics, and occurrence pattern, and supports effective use of local natural enemy resources in agroecosystems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Bridging the Urban-rural ‘Digital Gap’ for Agricultural Powerhouse: On the Establishment of National Data Bureau LI Yong, LI Qianchuan, ZHOU Yi Journal of Agricultural Big Data    2023, 5 (1): 15-17.   DOI: 10.19788/j.issn.2096-6369.230106 Abstract （187）   HTML （14）    PDF（pc） （324KB）（239）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Establishing New Development Pattern of Agricultural Big Data by Unlocking Data Flow ZHANG Lixiang, CUI Hanyu Journal of Agricultural Big Data    2023, 5 (1): 21-23.   DOI: 10.19788/j.issn.2096-6369.230108 Abstract （126）   HTML （7）    PDF（pc） （312KB）（236）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Construction and Application of Monitoring Platform for Saline-alkali Land in Cold Regions ZHANG HaiFeng, ZHANG Yu, LAI YongCai, ZHENG YanYan, LIU Kai, BI HongWen Journal of Agricultural Big Data    2023, 5 (2): 122-131.   DOI: 10.19788/j.issn.2096-6369.230217 Abstract （133）   HTML （8）    PDF（pc） （4593KB）（236）       Knowledge map    Save Saline-alkali land is an important reserve arable land resource in the country. Selecting and breeding salt-tolerant rice varieties in cold regions, and developing and utilizing saline-alkali land are important ways to promote food production and ensure national food security. In accordance with the national guiding spirit of "transforming from controlling saline-alkali land adaptation crops to selecting saline-alkali-tolerant plants to adapt to saline-alkali land, tapping the potential for saline-alkali land development and utilization, and striving to achieve breakthroughs in key core technologies and important innovation fields", a series of issues such as the imperfect technical system for saline-alkali land improvement have been addressed through collaborative research using new generation information technologies such as the Internet of Things and big data. A monitoring platform for saline-alkali land in cold regions has been built that integrates dynamic monitoring of the meteorological and soil environment of saline-alkali land, collection of phenotypic data of rice in cold regions, and visual display of data, achieving the integration of information technology in multiple disciplines such as germplasm resources, biotechnology, and soil fertilizers. This article introduces the digital monitoring methods and key technologies of data collection content for the improvement and utilization of saline-alkali land in cold regions from the perspective of platform architecture and functional implementation. The platform has been applied in different accumulated temperature zones such as Daqing, Jiamusi, and Heihe in Heilongjiang Province. The results show that the platform can assist scientific researchers to timely and comprehensively grasp the meteorological and soil information of saline-alkali plots, effectively reduce the impact of adverse environmental factors, and form a complete data chain; The platform aggregates and processes phenotypic traits, yield and quality data of rice germplasm resources at different phenological stages, excavates the environmental stress response rules of germplasm resources, and guides the scientific research and production of rice in saline-alkali lands, providing information technology support for the development and utilization of saline-alkali land resources. And realizing digital monitoring of saline-alkali-tolerant rice screening and soil improvement in cold regions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （115）   HTML （25）    PDF（pc） （2433KB）（232）       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）

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Theory and Engineering Technology Implementation of Artificial Intelligence Retrieval Paradigm for Parameters of Remote Sensing Based on Big Data MAO KeBiao, YUAN ZiJin, SHI JianCheng, WU ShengLi, HU DeYong, CHE Jin, DONG LiXin Journal of Agricultural Big Data    2023, 5 (4): 1-12.   DOI: 10.19788/j.issn.2096-6369.230401 Abstract （268）   HTML （43）    PDF（pc） （1723KB）（227）       Knowledge map    Save In order to solve the "black box" problem of artificial intelligence application in geophysical parameter retrieval, and make artificial intelligence applications have physical significance, interpretability, and universality, the theory and technology of deep learning coupling physical and statistical methods are gradually being developed in various disciplinary fields. This study summarizes the author's more than 20 years of relevant research, and presents the artificial intelligence inversion paradigms and judgment conditions for remote sensing parameters based on the induction and deduction of the theory and judgment conditions of artificial intelligence geophysical parameter inversion paradigms. At present, a common problem encountered in many studies is that many artificial intelligence parameter retrieval uses theoretical simulation data to achieve high retrieval analysis accuracy, but the actual application retrieval accuracy is not ideal. Therefore, deep learning how to couple physical and statistical methods has become an urgent engineering and technical challenge that needs to be addressed. We will take passive microwave soil moisture and surface temperature retrieval as an example to illustrate that the accuracy of the physical model itself still needs to be greatly improved, or the simulated data only represents a small portion of the actual situation. We believe that there are significant limitations in using only physical models to simulate data for direct retrieval, and high-precision multi-source statistical data must be supplemented. At the same time, we can also improve the physical model by directly using deep learning to simulate data training and testing with actual data to verify the gap between the physical model and the actual situation, determine the errors of the physical model, and thus improve the physical model. Statistical methods are the most intuitive description of human beings, while physical methods summarize and generalize statistical methods. However, information or energy transmission in the real world is transmitted in quantum form, and many physical models have made many simplifications without depicting real physical phenomena well. Different neurons in deep learning are more suitable for describing and expressing the transmission methods of quantum information. Understanding the real world through calculus quantum information flow requires improving our cognitive thinking. How to collect data that meets the real situation (quantum information or energy transmission) is very important. We can fully utilize physical logic reasoning to construct physical formulas and statistical methods, and use big data thinking mode to improve the accuracy of geophysical parameter inversion under the guidance of paradigm theory and judgment condition framework. Proving through physical logic reasoning that the input variable can uniquely determine the output variable is a fundamental condition for forming a physically meaningful, interpretable, and universal retrieval or classification or prediction paradigm. Controlling the quality of collected data from the perspective of quantum information (energy) transmission is the key to achieving high-precision inversion engineering and technology for geophysical parameters. Improving the cognitive understanding of quantum information flow in calculus and identifying the limitations of physical models are of milestone significance for achieving high-precision inversion in artificial intelligence. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Application and Construction of Big Data Fusion Framework for Anti-poverty Monitoring: A Systematic View of Data, Models, and Applications Xin Wang, Leifeng Guo Journal of Agricultural Big Data    2022, 4 (2): 108-118.   DOI: 10.19788/j.issn.2096-6369.220216 Abstract （346）   HTML （9）    PDF（pc） （1163KB）（224）       Knowledge map    Save Data-driven scientific decision-making ability is playing an increasingly important role in the prevention of returning to poverty in the context of rural revitalization. By stepping up research on data fusion issues in this area, in the big data of anti-poverty monitoring, the scientific decision-making system for preventing poverty return linked by data fusion model can be effectively established. There are three key problems in big data fusion of anti-poverty monitoring. The first is raw data analysis. The raw data of big data for anti-poverty monitoring is very complex. Due to the multi-dimension of the measurement standard of returning to poverty, the diversification of returning to poverty factors and other four aspects. As a result, its data sources have multiple industry sectors and multiple fields of expertise, and Its data features are spatio-temporal big data with multi-scale and multi-source heterogeneity. The second is the establishment of data fusion framework. In theory, the data fusion framework of big data for anti-poverty monitoring includes five levels: data source, monitoring target, data type, fusion model and data fusion application, thus, the framework of data fusion is constructed from the perspective of data-driven scientific decision. The third is data fusion application. In the whole process of monitoring and helping poverty prevention, through data fusion, big data of anti-poverty monitoring can provide scientific decision-making assistance for four aspects and nine specific target needs, including family user portraits and knowledge maps, identification and prediction of targeted objects, design of precise help strategies, prediction of poverty alleviation time and dynamic exit evaluation. The above research results systematically cover the data, models and applications in the big data fusion framework for anti-poverty monitoring, and further creatively and systematically put forward the big data fusion framework for anti-poverty monitoring on the basis of summarizing the existing research results. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design and Implementation of An Equine Intelligent Breeding Big Data Platform LIU YanHong, CAO KeTao, CHEN XinWen, LI JinXing, XIONG Tao, DU XueMei, BAI Tao, ZHENG WenXin, GUO LeiFeng Journal of Agricultural Big Data    2023, 5 (3): 93-103.   DOI: 10.19788/j.issn.2096-6369.230313 Abstract （126）   HTML （12）    PDF（pc） （3494KB）（223）       Knowledge map    Save With the continuous development of information technology, intelligent farming is being increasingly applied in modern livestock industry. Modern advanced information technology is gradually being applied throughout the entire process of equine farming. Utilizing technologies such as big data and artificial intelligence to promote the intelligent development of the equine industry and improve equine farming efficiency is one of the important pathways towards modernization and technological advancement of the equine industry. In this study, an equine intelligent farming big data platform based on the four-layer system architecture, including the device layer, data layer, data processing layer, and application layer, was developed using the latest information technologies such as big data, artificial intelligence, and the Internet of Things. The platform integrates five functional modules such as record management, epidemic prevention management, breeding management, behavior management, and environmental management, enabling data collection, analysis, model building, and application throughout the equine farming process. This research can provide insights for the construction of intelligent farming in equine breeding bases and enterprises, achieve fine monitoring and management of equine farming, improve equine production efficiency and breeding benefits, and provide more reference significance for the future development of the equine industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset of Coccinellidae in Farmlands in Harbin, Heilongjiang Province from 2018 to 2021 Xinglong Liu, Yu Wang, Xiaoxi Wang, Keqin Wang Journal of Agricultural Big Data    2022, 4 (4): 39-44.   DOI: 10.19788/j.issn.2096-6369.220405 Abstract （278）   HTML （13）    PDF（pc） （1076KB）（221）       Knowledge map    Save Predacious coccinellid are important natural enemies that control insect pests in farmlands. It can feed on the eggs, young larvae and pupae of aphids, psyllids, whiteflies, scale insects and some Lepidoptera and Coleoptera insects. Commonly seen species in fields include Harmonia axyridis, Propylea japonica, Hippodamia Variegata, Coccinella Septempunctata, etc. they have a strong continuous control effect on farmland pests. There are large crop cultivation areas and a variety of ecological land types in Heilongjiang, providing great potentials of biological control with natural enemies. In the present study, we requirements according to ' Data Center Monitoring Mission Specification for Natural Enemies and Other Insect Resources', and monitored seasonal coccinellid species and abundances in corn, soybean, and wheat fields in Harbin, and collected habitat situation information. We established the dynamic data set of ladybug population in Harbin farm. These data support further research on biodiversity and pest management in local agro-ecosystem. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Focusing on New Opportunities of Digital Transformation Closely to Promote Digital Village Construction WANG Xiaobing Journal of Agricultural Big Data    2023, 5 (1): 2-4.   DOI: 10.19788/j.issn.2096-6369.230102 Abstract （135）   HTML （19）    PDF（pc） （239KB）（220）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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The Big-data-drived Agricultural Industrial Internet SUN Tong, HUANG Huiheng Journal of Agricultural Big Data    2023, 5 (1): 18-20.   DOI: 10.19788/j.issn.2096-6369.230107 Abstract （118）   HTML （26）    PDF（pc） （229KB）（217）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Study on Drought Monitoring and Spatiotemporal Change in Henan Province Based on Sun/solar-induced Chlorophyll Fluorescence Remote Sensing ZHANG Zhaoxu, XIAO Yue, GOU Wentao, CUI Jin Journal of Agricultural Big Data    2023, 5 (1): 76-86.   DOI: 10.19788/j.issn.2096-6369.230116 Abstract （128）   HTML （6）    PDF（pc） （6400KB）（214）       Knowledge map    Save Drought, a natural disaster that has occurred frequently in recent decades, not only causes damage to the natural environment such as soil degradation, but also has a huge impact on economic development. The occurrence of drought is a long-term, continuous and complex process, which is the result of the combined effect of the atmosphere, soil and crops. This paper selected sun/solar-induced chlorophyll fluorescence remote sensing data from 2001 to 2020, took Henan Province as the study area, used the multi-year chlorophyll fluorescence anomaly index as the drought index, and classified drought classes based on the idea of quantile. Finally, the correlations between chlorophyll fluorescence anomaly index and statistical data (yield, affected area and disaster area) were calculated, and the spatial and temporal variation characteristics of the multi-year drought situation were analyzed, to provide scientific and effective suggestions for drought resistance and prevention. Results showed that the correlation coefficients of chlorophyll fluorescence drought index and wheat yield, maize yield, affected area and disaster area were 0.93, 0.89,-0.54 and-0.58, respectively. The correlation coefficients of chlorophyll fluorescence drought index and wheat yield and maize yield showed high positive correlation, while this index and affected area and disaster area showed negative correlation. This indicated the feasibility of chlorophyll fluorescence index in monitoring drought. Using the chlorophyll fluorescence drought index, the drought situation in Henan Province was calculated in practical and spatial dimensions, and the drought from 2001 to 2020 was analyzed. The overall degree of drought in Henan Province was reduced, and the extent of drought was significantly reduced. Finally, four measures were proposed for drought prevention based on the results. This paper provided a scientific basis for drought prevention and drought control based on remote sensing of sun/solar-induced chlorophyll fluorescence for drought monitoring and analysis in Henan Province. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis on the Development Trend of Breeding Intelligent Equipment Under the Background of New-Generation Information Technology HU TianCi, WANG WenSheng, QI JingWei, JIANG ChengXiang, CHEN XinWen, ZHENG WenXin, GUO LeiFeng Journal of Agricultural Big Data    2023, 5 (3): 56-68.   DOI: 10.19788/j.issn.2096-6369.230310 Abstract （112）   HTML （11）    PDF（pc） （3802KB）（213）       Knowledge map    Save In the current background of national promotion of the digital agriculture and smart farming, the breeding intelligent equipment began to develop vigorously. Using intelligent equipment in large-scale farms can improve breeding efficiency and reduce labor costs. At present, the domestic intelligent equipment has not been popularized, the domestic research on intelligent equipment is not sufficient, some of the intelligent equipments are still in the research and development stage, which can not reday for mass-scale production. Taking cattle and sheep breeding as example, the paper 1) summarizes breeding intelligent equipment and equipment function introduction demanded in the various stages of farming, 2) lists some of the current domestic research in the intelligent equipment, 3) introduces the impact of the new-generation technology, such as big data, artificial intelligence, etc. on the breeding intelligent equipment, 4) discusses the significance and existing problems of the domestic current development of breeding intelligent equipment, and 5) analyses the future development of the breeding intelligent equipment under the current background. The breeding intelligent equipment can accelerate the development of China's digital animal husbandry and improve the economic benefits of animal husbandry, which embraces the expansive prospects. Moreover, the breeding intelligent equipment can be used as hardware support for intelligent breeding and precision feeding, laying the foundation for the construction of a new generation of intelligent farm construction. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Dataset of the Statistics on Tomato Transaction Price in the Wholesale Market in Beijing-Tianjin-Hebei Region from 2012 to 2019 KONG FanTao, AN Min, SUN Wei, LIU JiFang, HU Lin, CAO ShanShan Journal of Agricultural Big Data    2023, 5 (2): 62-67.   DOI: 10.19788/j.issn.2096-6369.230209 Abstract （119）   HTML （19）    PDF（pc） （1011KB）（202）       Knowledge map    Save Tomato，one of the vegetables popular among Chinese residents，is widely grown throughout the country. In recent years, the price of tomato has shown the characteristics of large fluctuation range and instability. Hebei is the main production area of tomato, while Beijing and Tianjin are important sales areas of tomato. Taking the Beijing-Tianjin-Hebei region as a whole, the historical price data of tomato within the region are analyzed to explore the fluctuations of tomato market price. It is of great significance to perfect circulation pattern of regional agricultural products, balance tomato production structure and stabilize tomato market price. This data set fully combines the vegetable price data provided by the National Agricultural Product Business Information Public Service Platform, the National Wholesale Market Price Information System and other platforms, and the field survey data of the fund project team. Through comparison and integration with the field survey data, the authenticity and consistency of the basic data set are ensured. Through processing the daily average price information of tomato in 29 wholesale markets of agricultural products in the Beijing-Tianjin-Hebei region from 2012 to 2019, the text data and image data reflecting the price information of tomato wholesale markets in the Beijing-Tianjin-Hebei region from 2012 to 2019 were obtained, including weekly and monthly average price of tomato market, a total of 1536 articles; There are 78 historical fluctuation charts of weekly average price and monthly average price, and line charts of month-on-month and year-on-year price change rates. The obtained results have been tested by manual quality, and the data processing has been completed in accordance with standardized steps. The obtained data sets can provide basic data support for the research and monitoring and early warning of tomato market price fluctuations in the Beijing-Tianjin-Hebei region. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Fruit Quality Analysis of 23 Apple Germplasm Resources Simiao Sun, Yuan Gao, Kun Wang, Dajiang Wang, Lianwen Li Journal of Agricultural Big Data    2022, 4 (2): 13-19.   DOI: 10.19788/j.issn.2096-6369.220202 Abstract （241）   HTML （7）    PDF（pc） （673KB）（194）       Knowledge map    Save Objective In recent years, fruit quality is the main factor of the apple market competitiveness, and the diversity of fruit quality traits in different apple germplasm resources is studied and analyzed to cultivate new varieties and create new germplasm. Methods Twenty-three apple germplasms were evaluated for differences in fruit quality indicators including individual fruit weight, firmness without skin, soluble solid content, vitamin C, soluble sugar content, titratable acidity，TSS/TA. Meanwhile, these quality indicators were analyzed by correlation analysis and principal component analysis (PCA). Results The results showed that the seven quality indicators revealed a significant difference among 23 different apple germplasm. There is a significantly positive correlation between titratable acidity and vitamin C. There is a significantly positive correlation between titratable acidity and individual fruit weight. There is a significantly negative correlation between TSS/TA and vitamin C. There is a significantly negative correlation between TSS/TA and titratable acidity as well. Three major components with a characteristic value larger than 1 during PCA were extracted and the cumulative variance contribution rate was 73.21%. The first principal component represented TSS/TA, vitamin C, titratable acidity; the second principal component represented individual fruit weight; the third principal component represented firmness without skin, soluble solid content, soluble sugar content. Conclusion There is significant difference in seven fruits quality traits of twenty-three apple germplasm resources. Different germplasm has different typical traits. In these 23 apple germplasm resources, the fruit size of 'Yan Guang' is the highest. 'Huang Taiping' has the highest firmness without skin and soluble solid content. ‘Suan Wang' has the highest content of acid and vitamin C. ‘Zhang Jiakouduanzhi' has the highest soluble sugar content. The TSS/TA of 'Guo Shuai' is the highest. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Rule of Law Safeguards for Agricultural and Rural Data Security in the Context of Digital Village Construction FEI YanYing, ZHANG XuFan Journal of Agricultural Big Data    2023, 5 (3): 11-18.   DOI: 10.19788/j.issn.2096-6369.230303 Abstract （130）   HTML （21）    PDF（pc） （330KB）（193）       Knowledge map    Save The construction of digital villages is facing data security risks. The study explored the rule of law needs of agricultural and rural data security in the context of the construction of digital villages, sorted out and analyzed the current situation of the rule of law in agricultural and rural data security. To address the rule of law dilemma in agricultural and rural data security, the study proposed rules of law countermeasures to safeguard agricultural and rural data security by perfecting the legal system of agricultural and rural governmental data security and open sharing, perfecting the agricultural and rural enterprises' data security protection system, and raising the data security awareness of the rural population which will help safeguard the security of agricultural and rural data at the level of the rule of law and assist in the construction of digital villages. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification ZHANG MengMeng, WANG XiuJuan, KANG MengZhen, HUA Jing, WANG HaoYu, WANG FeiYue Journal of Agricultural Big Data    2023, 5 (4): 13-23.   DOI: 10.19788/j.issn.2096-6369.230402 Abstract （143）   HTML （22）    PDF（pc） （1259KB）（188）       Knowledge map    Save Accurate and efficient identification of crop diseases can enable farmers to take effective and targeted preventive measures in a timely manner, which is helpful to reduce the risk of yield reductions and economic losses caused by crop diseases. However, the recognition model that can achieve the effect of SOTA in other fields, especially in the application of rice disease identification, faces the challenge of insufficient available rice disease data, a limited range of disease varieties and low data quality. In this paper, a variety of classical convolutional neural networks are trained on two different datasets using transfer learning methods. We demonstrated that in addition to the optimization achieved through model structure, the training data set itself has an important impact on the training results. However, the scarcity of open-source agricultural data, coupled with the absence of a comprehensive open-source data sharing platform, remains a substantial obstacle. This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data, low level of education of most employees, underdeveloped distributed training systems and unsecured data security. To solve those challenges, this paper proposed a novel idea to construct an agricultural data sharing platform based on federated learning framework, aiming to address the deficiency of high-quality data in agricultural field training. Table and Figures | Reference | Related Articles | Metrics | Comments（0）