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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 （1899）   HTML （102）    PDF（pc） （1468KB）（3073）       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）

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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 （1734）   HTML （247）    PDF（pc） （4768KB）（2488）       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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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 （1297）   HTML （97）    PDF（pc） （1011KB）（2423）       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）

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Research and Application of Big Data in Agriculture Jiang Hou, Yang Yaping, Sun Jiulin Journal of Agricultural Big Data    2019, 1 (1): 5-10.   DOI: 10.19788/j.issn.2096-6369.190101 Abstract （2324）   HTML （231）    PDF（pc） （2372KB）（2394）       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）

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Progress in the Application of Big Data in Agriculture in China Zhou Guomin Journal of Agricultural Big Data    2019, 1 (1): 16-23.   DOI: 10.19788/j.issn.2096-6369.190102 Abstract （2393）   HTML （237）    PDF（pc） （915KB）（2330）       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）

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Big Data of Plant Phenomics and Its Research Progress Chunjiang Zhao Journal of Agricultural Big Data    2019, 1 (2): 5-14.   DOI: 10.19788/j.issn.2096-6369.190201 Abstract （3022）   HTML （237）    PDF（pc） （1041KB）（2247）       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）

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Accurate Precipitation Nowcasting with Meteorological Big Data: Machine Learning Method and Application Zhang Chenyang, Yang Xuebing, Zhang Wenshen Journal of Agricultural Big Data    2019, 1 (1): 78-87.   DOI: 10.19788/j.issn.2096-6369.190108 Abstract （1353）   HTML （34）    PDF（pc） （5172KB）（1990）       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）

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The Development Direction of Fresh Agricultural Products Supply Chain in China after COVID-19 Dongnan Li, Guoyang Pan, Qian Zhou, Bin Li Journal of Agricultural Big Data    2020, 2 (3): 42-51.   DOI: 10.19788/j.issn.2096-6369.200305 Abstract （2437）   HTML （79）    PDF（pc） （687KB）（1933）       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）

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Research and Design of Agricultural and Rural Digital Resources Architecture Dong Tang Wenfeng Du Weicheng Sun Guangrong Jia Xinwei Liang Journal of Agricultural Big Data    2019, 1 (3): 28-37.   DOI: 10.19788/j.issn.2096-6369.190303 Abstract （1129）   HTML （43）    PDF（pc） （586KB）（1723）       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）

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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 （554）   HTML （10）    PDF（pc） （933KB）（1564）       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）

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Design of an Agricultural Product Supply Chain Management System using Blockchain Technology Chenxue Yang, Zhiguo Sun Journal of Agricultural Big Data    2020, 2 (2): 74-83.   DOI: 10.19788/j.issn.2096-6369.200208 Abstract （1379）   HTML （60）    PDF（pc） （1013KB）（1561）       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）

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Construction of an Agricultural Big Data Platform for XPCC Cotton Production Xin Lv, Bin Liang, Lifu Zhang, Fuyu Ma, Haijiang Wang, Yangchun Liu, Pan Gao, Zhangze, HouTongyu Journal of Agricultural Big Data    2020, 2 (1): 70-78.   DOI: 10.19788/j.issn.2096-6369.200109 Abstract （2131）   HTML （79）    PDF（pc） （1192KB）（1532）       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）

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A Framework for Agricultural Big Data Standards Yanmin Yao, Yuqi Bai Journal of Agricultural Big Data    2019, 1 (4): 76-85.   DOI: 10.19788/j.issn.2096-6369.190408 Abstract （1323）   HTML （54）    PDF（pc） （622KB）（1514）       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）

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China Dairy    2020, 0 (4): 51-53.   Abstract （935）      PDF（pc） （883KB）（1428）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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A Survey of Big Data Deep Learning Systems and a Typical Agricultural Application Lingxu Zhang,Rui Han,Wenming Li,Yinxue Shi,Chi Liu Journal of Agricultural Big Data    2019, 1 (2): 88-104.   DOI: 10.19788/j.issn.2096-6369.190208 Abstract （1436）   HTML （52）    PDF（pc） （2358KB）（1408）       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）

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The Etiology,Prevention and Treatment of Calf Pneumonia and Case Analysis CHEN Xijuan, XIAO Xidong, WU Shuang China Dairy    2022, 0 (4): 52-56.   DOI: 10.12377/1671-4393.22.04.10 Abstract （425）      PDF（pc） （1506KB）（1321）       Knowledge map    Save Calf pneumonia generally refers to inflammation caused by the lung infection of calves and is one of the common diseases of calves. The lobular pneumonia is the most common disease in calves within 2 months of age. The main cause of calf pneumonia is the invasion of pathogenic microorganisms. Irritation of adverse factors,blood infection,secondary to some diseases and inhalation of foreign bodies are common causes. The main clinical symptoms are relaxation fever or intermittent fever,pulmonary auscultation with crackling and rales,pulmonary percussion with focal dullness area,cough,accelerated breathing,dyspnea and so on. The emphasis of prevention is to protect calves,avoid the invasion of exogenous pathogenic microorganisms,and inhibit the proliferation of endogenous pathogenic microorganisms. The treatment focuses on antibacterial and anti-inflammatory, preventing exudation,promoting absorption,relieving cough and expectorating phlegm,antibiotics should be used to avoid drug resistance,drug use according to the course of treatment,combination of Chinese and Western medicine,and symptomatic and causal treatment. Reference | Related Articles | Metrics | Comments（0）

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Health Management for Pre-weaned Dairy Calves LI Shiqing, ZHANG Xin, YI Xia, ZHUANG Haohua, MA Chong China Dairy    2021, 0 (10): 9-18.   DOI: 10.12377/1671-4393.21.10.03 Abstract （371）      PDF（pc） （1280KB）（1274）       Knowledge map    Save The period of calf from birth through weaning is the beginning of dairy cattle's life cycle and the weakest period, can influence the replacement heifers and efficiency of dairy farm. The major causes of morbidity and mortality in dairy calves continue to be diarrhea,pneumonia,septicemia,navel ill,and et al. But diarrhea and pneumonia are the main challenges faced by dairy farmer all over the world. This article oriented the data of morbidity and mortality of calf,colostrum program,management of calf diarrhea and respiratory disease,and some recommendations of prevention and control strategy. Colostrum program includes factors associated with quality,colostrum collection,storage,treatment,and feeding. In the part of health management,we placed on management of maternity pen,environment,feeding,nutrition,vaccination program,ventilation,early detection of disease,stress,and management of sick calves. Reference | Related Articles | Metrics | Comments（0）

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Bulletin of Agricultural Science and Technology    2021, 0 (3): 10-11.   Abstract （2775）      PDF（pc） （364KB）（1259）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Status quo and Effectiveness Analysis of Internal Control in Huishan Dairy Industry LV Xinliang China Dairy    2022, 0 (5): 13-17.   DOI: 10.12377/1671-4393.22.05.03 Abstract （470）      PDF（pc） （2301KB）（1258）       Knowledge map    Save In December 2016,Muddy Waters issued a short report on Huishan Dairy,pointing out that the company had whitewashed statements and exaggerated financial leverage. In the months that followed,Huishan's share price plunged. The failure of internal control is the source of this Muddy Water short selling event. As the first barrier to ensure the normal production and operation of enterprises,it did not play its due role. In order to study the role of internal control in enterprise operation,the internal control situation of Huishan Dairy was analyzed through the framework of five elements of internal control. It is found that the enterprise has five problems: weak control environment,imperfect risk assessment mechanism,ineffective control activities, poor information communication and insufficient supervision. In view of these problems, corresponding optimization suggestions are put forward,hoping to provide some references and suggestions for the industry to optimize internal control. Reference | Related Articles | Metrics | Comments（0）

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The Chinese Livestock and Poultry Breeding    2023, 19 (4): 41-45.   Abstract （771）      PDF（pc） （1756KB）（1240）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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China Swine Industry    2018, 13 (2): 68-70.   DOI: 10.1111/1673-4645-2018-02-0068 Abstract （402）   HTML （14）    PDF（pc） （1387KB）（1185）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Spectral and Imaging Datasets of Apple Leaf Disease and Insect Pests in China in 2015 Fei Gao, Xiaoli Wang, Tingting Liu, Zhuang Li, Rui Man Journal of Agricultural Big Data    2020, 2 (4): 120-124.   DOI: 10.19788/j.issn.2096-6369.200415 Abstract （1594）   HTML （98）    PDF（pc） （1305KB）（1113）       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）

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Development Status and Trend of Low Temperature Fresh Milk Market in China Huo Xiaona China Dairy    2020, 0 (10): 22-25.   Abstract （414）      PDF（pc） （904KB）（1059）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Development and Visions of Blockchain Technology Hui Li, Yuming Yuan, Wenqi Zhao Journal of Agricultural Big Data    2020, 2 (2): 5-13.   DOI: 10.19788/j.issn.2096-6369.200201 Abstract （2240）   HTML （101）    PDF（pc） （1126KB）（1031）       Knowledge map    Save Blockchain (or distributed ledger) technology was introduced in 2008, when the famous Bitcoin cryptocurrency was initiated. Blockchain has been undergoing rapid growth in both academia and industry. Today, it is no exaggeration to say that blockchain has become a new, independent research topic—not a subtopic subsumed within cryptocurrencies. From a technical perspective, blockchain technology is based on various fundamental computing technologies, such as advanced cryptography, distributed data storage, peer-to-peer networking, and distributed consensus protocols. Generally, blockchain technology involves creating a shared, distributed ledger: that ledger can offer great flexibility and potential in resolving many important challenges in a complex computing context that involves multiple parties. Examples of such challenges include achieving mutual trust, privacy protection, and data consistency in large-scale business scenarios. Many business applications have already covered a broad range of industrial services, such as those related to finance, governance, medicine, and city construction. Blockchain technology is becoming increasingly adopted and applied; however, the current design of blockchain is practically far from sufficient—especially when dealing with critical domain challenges. Specifically, the key limitations of blockchain mainly derive from poor system scalability, weak resilience to external security attacks, and lack of computing interfaces for regulatory processes. Conversely, it is the very shortcomings of blockchain technology that motivate research efforts into many related technologies. Based on conventional blockchain design, new functional extensions and cryptography technical optimizations have been continuously proposed by researchers and practitioners: the aim is to make blockchain technology more practically applicable and meet various demands of different business users. In this overview paper, using the latest findings from both academic and industrial research, we systematically present the general architecture of blockchain technology with its five functional layers. The five-layered architecture comprises the following: a data layer; a network layer; a distributed consensus layer; a smart contract layer; and an application layer. We also provide a technical description of key theories and important techniques related to each functional layer. From the proposed general architecture of blockchain technology, we offer an in-depth explanation of its core technical extensions with respect to the following: blockchain integration with existing computer network techniques; the blockchain framework itself and important modules; and underlying critical cryptography techniques. Further, we discuss potential contributions that these promising technical extensions could provide with respect to reshaping and optimizing blockchain technology. Finally, following current developments with blockchain technology and its existing mainstream applications, the general views about future challenges and important directions for this technology is to facilitate future follow-up research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （1290）   HTML （63）    PDF（pc） （1052KB）（995）       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）

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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 （814）   HTML （34）    PDF（pc） （802KB）（948）       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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China Swine Industry    2013, 8 (1): 19-19.   DOI: 10.16174/j.cnki.115435.2013.01.035 Abstract （311）      PDF（pc） （372KB）（936）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Breast Parameters and Breast Shape of Kazak Horse in Altay of Xinjiang WANG Tao, LI Hangsen, Gullibhatti Dawuletihan, Inamujiang Ali Maimaiti, Nurmanu Krimu, BAI Xinyu, WANG Xu, Arqing Toliken, Geminguli Muhatai China Dairy    2022, 0 (2): 45-49.   DOI: 10.12377/1671-4393.22.02.07 Abstract （197）      PDF（pc） （1593KB）（922）       Knowledge map    Save In order to determine the milk use advantage of Kazakh horses in Altay of Xinjiang, 131 Kazakh horses during peak lactation period were measured in this study,and the characteristics and milking parameters were summarized. The results showed that the average circumference of the breast was 58.48 cm,the depth of the breast was 13.24 cm,the length of the breast groove was 24.70 cm,the length of the nipple was 4.27 cm,the circumference of the nipple root was 11.13 cm and the distance between the two nipples was 5.43cm. The average value of breast structure parameters of Kazak horses in Altai area is close to bowl breast,among which bowl breast ratio is 52.67%,bulbous breast ratio is 34.35%,piriform breast ratio is 12.98%. This ratio indicates that Kazak horses in Altai area have great milk potential. At the same time,the results of timing method were 127.21 times /min for manual milking,112.4 times /min for Suckling and 35.64 times /min for pulsing,which could optimize the times of manual milking and provide data support for the development of horse milking device. Reference | Related Articles | Metrics | Comments（0）

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China Swine Industry    2021, 16 (1): 83-87.   Abstract （225）      PDF（pc） （1625KB）（917）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Big Data Applications and Construction for the Shandong Potato Industry Jia Ruan Huaijun Feng Wenjie Zhao Journal of Agricultural Big Data    2020, 2 (1): 29-35.   DOI: 10.19788/j.issn.2096-6369.200104 Abstract （647）   HTML （10）    PDF（pc） （727KB）（899）       Knowledge map    Save This paper analyzes the existing data for the Shandong potato industry to understand its production, circulation, storage, and processing characteristics, among other elements. According to the data analysis of production in the Shandong potato industry, potatoes are planted in spring and autumn, with Tengzhou and Jiaozhou as the main planting areas. According to the data analysis of circulation, the market price fluctuation shows the rule of “three years/one cycle,” with outstanding and refined management benefits. Potato exports from Shandong are the highest among potato exports in China. According to the data analysis of storage, the potato storage capacity can still meet local needs and cold storage constructions have been developed to include more modern and constant temperature storage facilities. According to the data analysis of processing, improvement is still needed in processing capacity. This paper outlines the construction of a big data platform for a single variety of Shandong potato. Modern precision agriculture in production will develop a measurement system that integrates all production elements and spaces will be constructed to guide intelligent production. In the circulation element, online e-commerce trading and safety traceability systems for potatoes will be constructed in addition to developing the quality of agricultural products. For the storage element, a potato refrigeration–packaging industry will be promoted, including the accelerated establishment of cold storage, warehouse storage, and logistics alliances. In the processing element, a staple potato food processing industry will be developed alongside the promotion of the cluster development of processing enterprises. We will effectively strengthen top-level designs, consolidate and integrate research into key technologies for big data, and promote data sharing, opening up, development, and utilization. By speeding up the adjustment of regional layouts, preventing excessive fluctuation of potato prices, and strengthening the information analysis and early warning for the whole industry chain, we can better lead the sustainable and high-quality development of the Shandong potato industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Preliminary Exploration of Big Data Security Supported by Blockchain in Agriculture Zhongfu Sun, Juncheng Ma, Feixiang Zheng, Keming Du Journal of Agricultural Big Data    2020, 2 (2): 25-37.   DOI: 10.19788/j.issn.2096-6369.200203 Abstract （719）   HTML （24）    PDF（pc） （1259KB）（898）       Knowledge map    Save Big data constitutes a core technology for smart agriculture. However, there are a number of issues related to security with big data that could limit progress with smart agriculture. Following the speedy development of blockchain technology and in light of its intrinsic security characteristics. Big data could provide a new driving force and means for big data to be safely applied. This paper addresses some challenges related to supporting the safe development of big data. Briefly described the current status of China's agriculture and the background of the development of smart agriculture. Regarding basic issues concerning smart agriculture, a systematic study is made of the mutual relationship between agricultural big data security and blockchain technology which involves a brief introduction to the significance of big data, existing risks of big data, current challenges, and measures to promote bigdata safety. Comprehensive analyzed the security characteristics of the blockchain, described the supporting status and roles of blockchain in big data security, and explained three core directions of blockchain data security, which are data confidentiality, integrality, and availability. In conclusion, proposed suggestions and future applications on how to use blockchain to help the security of big data development. Reference | Related Articles | Metrics | Comments（0）

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Bulletin of Agricultural Science and Technology    2018, 0 (5): 12-14.   DOI: 10.3969/j.issn.1000-6400.2018.05.012 Abstract （838）   HTML （38）    PDF（pc） （604KB）（897）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Financial Risk Analysis Based on Z-score Model -- Taking Xinjiang Tianrun Dairy Co.,Ltd as an Example Ayidana·BALEKATI, CHEN Changming China Dairy    2021, 0 (12): 11-17.   DOI: 10.12377/1671-4393.21.12.02 Abstract （2792）      PDF（pc） （1643KB）（894）       Knowledge map    Save The Z-score model is a multivariate model proposed by Edward Altman to measure the financial risk of an enterprise. Taking Xinjiang Tianrun Dairy Co.Ltd.as an example,this paper combined the applicability of the Z-score model with the financial statement,using traditional financial accounting risk analysis with Z - score model financial risk analysis method,to analyze the cause of the financial risk. Furthermore,this work compared Xinjiang Tianrun Dairy Co.,Ltd with its leading corporation,Inner Mongolia Yili Industrial Group Co.,Ltd. in the same dairy industry and put forward corresponding countermeasures. The research results indicated that Xinjiang Tianrun Dairy Co.,Ltd. is in a healthy financial position,and the possibility of financial crisis in the short term is relatively low. But there is still a certain gap compared with Inner Mongolia Yili Industrial Group Co.,Ltd. which is the leading enterprise in the dairy industry. Reference | Related Articles | Metrics | Comments（0）

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China Dairy    2018, 0 (3): 8-10.   Abstract （403）      PDF（pc） （878KB）（869）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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China Swine Industry    2022, 17 (1): 38-49.   DOI: 10.16174/j.issn.1673-4645.2022.01.007 Abstract （5628）      PDF（pc） （1772KB）（857）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Common Quality Problems and Control Measures in The Production of Solidified Yoghurt Liu Yang, Yang Renqin, Xu Guangxin, Zhang Haixia, Gu Wenjing, Chen Jia China Dairy    2020, 0 (5): 67-69.   Abstract （309）      PDF（pc） （891KB）（844）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Report of Xinjiang Dairy Industry From 2019 to 2020 LIANG Chunming, XU Dong, SHAO Wei China Dairy    2021, 0 (9): 46-51.   DOI: 10.12377/1671-4393.21.09.07 Abstract （434）      PDF（pc） （2102KB）（836）       Knowledge map    Save By monitoring the changes of Xinjiang dairy cattle stock,the scale of dairy farms,the changes of milk purchase price,the proportion of dairy product types during 2019 to 2020 and analyzing the reasons of insufficient advantages of dairy variety resources,decline of total dairy cattle stock,high breeding cost,unreasonable dairy product structure,the problems of weak market development ability in Xinjiang.Reasonable suggestions such as continuing to promote large-scale breeding,carrying out accurate feeding,promoting high-quality forage production,optimizing product structure and increasing policy support are put forward. Reference | Related Articles | Metrics | Comments（0）

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China Dairy    2021, 0 (2): 21-24.   DOI: 10.12377/1671-4393.21.02.06 Abstract （276）      PDF（pc） （885KB）（831）       Knowledge map    Save Reference | Related Articles | Metrics | Comments（0）

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Progress in the Application of Big Data in fishery in China Jinxiang Cheng, Yingze Sun, Jing Hu, Xue Yan, Haiying Ouyang Journal of Agricultural Big Data    2020, 2 (1): 11-20.   DOI: 10.19788/j.issn.2096-6369.200102 Abstract （1130）   HTML （47）    PDF（pc） （615KB）（799）       Knowledge map    Save Big data has become an essential resource for green fisheries, and is an important focus for innovation in fisheries science and technology. Big data promotes the production, operation, management, and service provisions of fisheries, and plays a pivotal role in advancing the integration of primary, secondary, and tertiary industries in fisheries. China is the largest aquaculture country in the world, and attaches great importance to research and application development of big data in modern fisheries. For historical and practical reasons, fisheries big data is characterized by diversified resource channels, complex structures, uneven quality, wide application scope, and low overall data quality. Therefore, for research on, and application of fisheries big data in China, it is important to review the progress regarding the application of such data systematically, and clarify the direction of future development. Based on literature research and related scientific research practices, this article compares and analyzes the definitions of fisheries big data by different scholars; elaborates on the concept of fisheries big data; introduces the multiple sources and main characteristics of fisheries big data; and reviews the management and policy progress of fisheries big data. The development and application of fisheries big data in recent years have focused mainly on scientific research, aquaculture management, resource investigation, and economic circulation. In combination with engineering practice, this article explores the scenario application of fisheries big data. Finally, based on the current situation, it identifies the problems and challenges, and provides suggestions for further promoting the development of fisheries big data in China. Reference | Related Articles | Metrics | Comments（0）

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The Status and Trends of Scientific Data Sharing Systems Yunting Li, Liangming Wen, Lili Zhang, Jianhui Li Journal of Agricultural Big Data    2019, 1 (4): 86-97.   DOI: 10.19788/j.issn.2096-6369.190409 Abstract （1022）   HTML （33）    PDF（pc） （800KB）（795）       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）