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Antioxidant Activity of Solvent Extracts of Xinjiang Propolis and Determination of Quercetin and Chrysin Propolis Buwihelqem Ababekri, Mutallip Amet Aminigul, Mamat Nizam Ehet, Yimit Rahman Biotechnology Bulletin    2013, 1 (2): 163-171.   Abstract （173）      PDF（pc） （2778KB）（49575）       Knowledge map    Save To study the DPPH free radical scavenging activity and anti-lipid peroxidation activity of Kuqa and Yili, and determine the content of the Quercetin and Chrysin in Xinjiang Yili propolis. Different extracts of raw propolis were extracted by four kinds of solvents, taking tea polyphenol as comparison; measure Kuqa and Yili, Xinjiang propolis extracts antioxidant activity in removal of DPPH（1, 1-Diphenyl- 1-picrylhydrazyl）free radicals and antioxidant properties of linoleic acid peroxidation. With 70% ethanol and methanol as solvent to extract Quercetin and Chrysin from Xinjiang Yili propolis, using HPLC（high performance liquid chromatography）to determine the content of the extracted Quercetin and Chrysin, the detection wavelength was 370 nm and 268 nm, respectively. Result showed that propolis different extracts in different concentrations has different scavenging free radical DPPH capacity, in partial experimental groups’ scavenging capacity is even surpassed to same concentration of tea polyphenol group; has antioxidant activity to linoleic acid, in which Yili propolis has higher antiperoxidation activity. The content of Quercetin was the lowest in the without defatted ethanol extracts（2.2950 mg/g）, the highest in the defatted methanol extracts（2.8150 mg/g）；The content of Chrysin was the lowest in the without defatted methanol extracts（60.72 mg/g）, the highest in the defatted methanol extracts（74.37 mg/g）. The Kuqa and Yili propolis has a certain extent of antioxidant capacity, this DPPH method is fast, sensitive and simple, this results showed that the Xinjiang propolis is a natural antioxidant, and waiting for further study and utilization; HPLC analysis showed that Quercetin and Chrysin the active ingredients of propolis samples from Xinjiang Yili were detected. 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 （3301）   HTML （215）    PDF（pc） （359KB）（20230）       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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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 （1244）   HTML （39）    PDF（pc） （4374KB）（10475）       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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Tomato Dataset for Agricultural Scene Visual-Parsing Tasks Lingli Zhou, Ni Ren, Wenxiang Zhang, Yawen Cheng, Cheng Chen, Zhongyi Yi Journal of Agricultural Big Data    2021, 3 (4): 70-76.   DOI: 10.19788/j.issn.2096-6369.210408 Abstract （2486）   HTML （143）    PDF（pc） （1003KB）（8782）       Knowledge map    Save Agricultural robots are an important part of the development of agricultural modernization, and computer vision technology effectively promotes their application in the field of agriculture by perceiving and analyzing crops and the environment. However, because of the complexity and diversity of agricultural scenes, the detailed and annotated large-scale image datasets required by advanced computer vision methods are scarce in the field of agriculture. This lack of datasets is the main challenge in the development of computer vision technology in the field. To solve this problem, this paper presents a large-scale tomato image dataset that can be used for semantic image segmentation, instance segmentation, target detection, and other tasks. The dataset consists of synthetic and real images. The synthetic images include 3250 synthetic tomato images and the corresponding pixel-level semantic segmentation label images; the real images consist of 750 monocular images and 400 binocular images taken by RGB cameras, some of which have detailed manual labels for instance segmentation and target detection. This research aims to enrich many aspects of the dataset, including its capacity, the dimensionality of the annotation information, and the complexity of the scene, and to provide data support for solving future problems in the field of agriculture using computer vision technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （2454）   HTML （89）    PDF（pc） （802KB）（8086）       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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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 （4692）   HTML （326）    PDF（pc） （1041KB）（7873）       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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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 （3396）   HTML （163）    PDF（pc） （1468KB）（7256）       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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Comparison of Visualization Principles between Citespace and VOSviewer FU Jian, DING Jingda Journal of library and information science in agriculture    2019, 31 (10): 31-37.   DOI: 10.13998/j.cnki.issn1002-1248.2019.10.19-0776 Abstract （5002）      PDF（pc） （1577KB）（6663）       Knowledge map    Save With the rapid development of social informatization and the arrival of the era of big data, scientific 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 （4450）   HTML （507）    PDF（pc） （4768KB）（6244）       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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Construction Process and Technological Prospects of Large Language Models in the Agricultural Vertical Domain ZHANG YuQin, ZHU JingQuan, DONG Wei, LI FuZhong, GUO LeiFeng Journal of Agricultural Big Data    2024, 6 (3): 412-423.   DOI: 10.19788/j.issn.2096-6369.000052 Abstract （1575）   HTML （105）    PDF（pc） （1315KB）（5817）       Knowledge map    Save With the proliferation of the internet, accessing agricultural knowledge and information has become more convenient. However, this information is often static and generic, failing to provide tailored solutions for specific situations. To address this issue, vertical domain models in agriculture combine agricultural data with large language models (LLMs), utilizing natural language processing and semantic understanding technologies to provide real-time answers to agricultural questions and play a crucial role in agricultural decision-making and extension. This paper details the construction process of LLMs in the agricultural vertical domain, including data collection and preprocessing, selecting appropriate pre-trained LLM base models, fine-tuning training, Retrieval Augmented Generation (RAG), evaluation. The paper also discusses the application of the LangChain framework in agricultural Q&A systems. Finally, the paper summarizes some challenges in building LLMs for the agricultural vertical domain, including data security challenges, model forgetting challenges, and model hallucination challenges, and proposes future development directions for agricultural models, including the utilization of multimodal data, real-time data updates, the integration of multilingual knowledge, and optimization of fine-tuning costs to further promote the intelligence and modernization of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Transcriptome and RNA-Seq Technology Zhang Chunlan, Qin Zijuan, Wang Guizhi, Ji Zhibin, Wang Jianmin Biotechnology Bulletin    2012, 0 (12): 51-56.   Abstract （793）      PDF（pc） （1420KB）（5764）       Knowledge map    Save The transcriptome is the complete set of transcripts for certain type of cells or tissues in a specific developmental stage orphysiological condition. Transcriptome analysis can reveal the organism's gene expressing level, structural variation, discovery of new genes. Theresearch methods and platforms of transcriptome are undergoing rapid changes. And bioinformatics analysis is also improved gradually. RNASeqas a new research method, it can be more quickly and afford more accurate transcriptome’s information while using the Next-generationSequencing（NGS）technology. This article compares several main methods and platforms of transcriptome in recent years, and review theprinciple, purpose, steps, bioinformatics’ analysis and applications in related fields of RNA-Seq. This will be afford useful reference for relatedresearch. Reference | Related Articles | Metrics | Comments（0）

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State-of-the-art and recommended developmental strategic objectivs of smart agriculture Zhao Chunjiang Smart Agriculture    2019, 1 (1): 1-7.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA005 Abstract （7250）   HTML （16830）    PDF（pc） （433KB）（5316）       Knowledge map    Save With the wide applications of modern information technology in agriculture, agricultural intelligent technology revolution with manifestation of smart agriculture is coming. Smart agriculture is an advanced stage in the development of agricultural informatization from digitalization to networking to intelligence, it forms a new way of agricultural production, i.e., taking information and knowledge as the core elements, and integrating modern information technology such as internet, internet of things (IoT), big data, cloud computing, artificial intelligence, intelligent equipment, and so on, to realize agricultural information perception, quantitative decision-making, intelligent control, precise input, and personalized service. Smart agriculture is a milestone in the development of agriculture and has become the development trend of modern agriculture in the world. In this article, the policies, measures, and programs for encouraging the development of smart agriculture issued by Japan, the European Union, the United Kingdom, Canada, the United States, and other countries and regions were summarized, the development history from 1.0 version to 4.0 version of agriculture and development status of smart agriculture in China were also analyzed: remarkable results has achieved, at the end of 2017, the proportion of internet access in administrative villages reached 96%, 204,000 villages established the AgroSciences Information Agency, the retail sales of rural networks reached RMB 1.25 trillion Yuan, 426 cost-effective agricultural IoT products and technologies have been formed by the implementation of IoT pilot project. Behind the rapid development, smart agriculture in China still faces the problems of lack of basic research and technology accumulation, technologies such as sensors for agriculture, animal and plant models with intelligent decision-making, intelligent and precise operation equipment are the main short-boards. The pilot construction projects for the application of smart agriculture have been carried out all over the country, however, the role of display was greater than the actual effect. In order to solving the problems and achieving development demand, the strategic objectives of breaking through the core technologies, realizing the three major changes of "machine replacing manpower", "computer replacing human brain", and "independent technology replacing imports", improving the agricultural production level of intelligence and management network, accelerating the popularization of information services, and reducing application cost, providing farmers with personalized and precise information services that are affordable, and well-used, greatly improving agricultural production efficiency, and guiding the development of modern agriculture were proposed. Based on the analysis above, finally, eight key tasks including developing agricultural sensors, large-load agricultural UAV (unmanned aerial vehicle) protection systems, smart tractors, agricultural robots, agricultural big data, agricultural artificial intelligence, integrated applications and smart agricultural industry, and five policy recommendations including strengthening government support, formulating relevant subsidy policies, strengthening technical standards, and opening data sharing for the future development of smart agriculture in China were proposed. Reference | Related Articles | Metrics | Comments（0）

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Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing Lan Yubin, Deng Xiaoling, Zeng Guoliang Smart Agriculture    2019, 1 (2): 1-19.   DOI: 10.12133/j.smartag.2019.1.2.201904-SA003 Abstract （3876）   HTML （5940）    PDF（pc） （1165KB）（5235）       Knowledge map    Save Rapid acquisition and analysis of crop information is the precondition and basis for carrying out precision agricultural practice. Variable spraying and agricultural operation management based on the actual degree of crop diseases, pests and weeds can reduce the cost of agricultural production, optimize crop cultivation, improve crop yield and quality, and thus achieve precise agricultural management. In recent years, with the rapid development of UAV industry, UAV agricultural remote sensing technologies have played an important role in monitoring crop diseases, insects and weeds because of high spatial resolution, strong timeliness and low cost. Firstly, this research introduces the basic idea and system composition of precision agricultural aviation, and the status of UAV remote sensing in precision agricultural aviation. Then, the common UAV remote sensing imaging and interpreting methods were discussed, and the progress of UAV agricultural remote sensing technologies in detecting crop diseases, pests and weeds were respectively expounded. Finally, the challenges in the development of UAV agricultural remote sensing technologies nowadays were summarized, and the future development directions of UAV agricultural remote sensing were prospected. This research can provide theoretical references and technical supports for the development of UAV remote sensing technology in the field of precision agricultural aviation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Applications and Prospect Analysis of Deep Learning in Plant Genomics and Crop Breeding HOU Xiangying, CUI Yunpeng, LIU Juan Journal of library and information science in agriculture    2022, 34 (8): 4-18.   DOI: 10.13998/j.cnki.issn1002-1248.22-0101 Abstract （157）      PDF（pc） （1977KB）（5102）       Knowledge map    Save [Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plant genomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. As powerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes. In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, a complete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied to genomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced the workflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includes the selection of model architecture and hyperparameters; model evaluation and interpretability. Specifically, this paper introduces the background of deep learning approaches, including the latest graph neural networks; then it discusses two prominent issues in the intersection of genomics and deep learning with respect to gene characterization and protein characterization: 1) how to model the flow of information from plant genomic DNA sequences to molecular phenotypes; and 2) how deep learning models can be utilized to identify functional variation in natural populations? Specifically, the paper summarizes the current status of deep learning applications in related fields, which include deep learning and DNA and gene characterization research, interpretability of deep learning in genomics applications, graph neural networks in genomics, deep learning and genomic variation research, deep learning in protein prediction, ALPHAFOLD in protein prediction, deep learning and crop breeding research, and unsupervised learning in genomics and protein characterization. [Results/Conclusions] This article summarizes how traditional deep-learning algorithms, graph deep-learning, generative adversarial networks and interpretable AI are applied in current research in order to address these two problems. Finally, the prospects for deep learning in future plant genomics research and crop improvement are discussed. Overall, deep learning has provided better results than conventional methods in many genomics research directions, and the application of deep learning in genomics has yielded early applications of scientific and economic significance. Deep learning offers two distinct advantages: 1) end-to-end learning, with the ability to integrate multiple pre-processing steps into a single model; and 2) multimodal data processing capabilities that can handle extremely heterogeneous data in genomics. The advancement of deep learning has the potential to expand new research perspectives in genomics and crop breeding, and to facilitate larger-scale association studies in both phenotypic and genotypic genomics as algorithms become more accurate. Reference | Related Articles | Metrics | Comments（0）

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Research on APP User Behavior Based on Modified UTAUT and TTF Model ——Taking the University Mobile Library as an Example LI Ziqiang, ZHANG Meiling, YE Weijiao, CHEN Youcheng Journal of library and information science in agriculture    2019, 31 (9): 51-59.   DOI: 10.13998/j.cnki.issn1002-1248.2019.06.19-0552 Abstract （662）      PDF（pc） （1775KB）（4800）       Knowledge map    Save [Objective] This paper discusses the factors affecting the adoption behavior of college students' mobile libraries from the perspective of user behavior, and provides reference for the construction and development of mobile libraries. [Method] Based on the UTAUT and TTF models, this paper introduces the perceptual entertainment variables to modify the model, constructs the research model of the influencing factors of mobile library adoption behavior, and uses the structural equation model to analyze the path of 271 college students' questionnaire data. [Conclusion] The task/technical adaptability has a significant positive impact on the perceived usefulness of mobile libraries; perceived usefulness, perceived ease of use, perceived entertainment, and task/technical fitness all have significant positive impacts on mobile library adoption expectations, and the willingness to adopt plays a mediating role in the impact of the above variables on the adoption behavior of mobile libraries; convenience conditions and willingness to adopt directly have a significant positive impact on the adoption behavior of mobile libraries.Based on this, four suggestions for decision making are proposed. 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 （1260）   HTML （84）    PDF（pc） （911KB）（4720）       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 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 （1127）   HTML （80）    PDF（pc） （3734KB）（4200）       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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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 （1870）   HTML （52）    PDF（pc） （5172KB）（3887）       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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An Overview of Zero-Knowledge Proof Technology and Its Typical Algorithms and Tools WAN Wei, LIU JianWei, LONG Chun, LI Jing, YANG Fan, FU YuHao, YUAN ZiMeng Journal of Agricultural Big Data    2024, 6 (2): 205-219.   DOI: 10.19788/j.issn.2096-6369.200002 Abstract （967）   HTML （42）    PDF（pc） （517KB）（3817）       Knowledge map    Save In the context of the increasing importance of data security and privacy protection, Zero-Knowledge Proofs (ZKPs) have provided a powerful tool for protecting privacy. This article comprehensively discusses the technology of zero-knowledge proofs and their application in modern cryptography. First, the article introduces the basic concepts of zero-knowledge proofs, as well as different types of ZKPs such as Snarks and Starks, along with their technical characteristics and application scenarios. In particular, the article conducts an in-depth study of ZK-Snarks. At the same time, the article also discusses other proof mechanisms such as ZK-Stark and Bulletproofs, comparing their differences in design and performance. Then, it focuses on the application of ZKPs in the blockchain environment and analyzes the related tools for writing zero-knowledge proofs. Finally, it points out some potential problems and future research directions in the field of zero-knowledge proofs. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （1991）   HTML （140）    PDF（pc） （362KB）（3805）       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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Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture LI Daoliang, LIU Chang Smart Agriculture    2020, 2 (3): 1-20.   DOI: 10.12133/j.smartag.2020.2.3.202004-SA007 Abstract （3564）   HTML （6988）    PDF（pc） （2843KB）（3789）       Knowledge map    Save The production of China's aquaculture has changed from extensive model to intensive model, the production structure is continuously adjusting and upgrading, and the production level has been continuously improved. However, as an important part of China's agricultural production, aquaculture plays an important role in promoting the development of China's agricultural economy. Low labor productivity, production efficiency and resource utilization, low-quality aquatic products, and the lack of safety guarantees have severely limited the rapid development of China's aquaculture industry. Using modern information technology and intelligent devices to realize precise, automated, and intelligent aquaculture, improving fishery productivity and resource utilization is the main way to solve the above contradictions. Artificial intelligence technology in aquaculture is to use the computer technology to realize the production process of aquaculture, monitor the growth of underwater organisms, judge, discuss and analyze problems, and then perform feeding, disease treatment, and breeding. In order to understand the development status and technical characteristics of artificial intelligence technology in aquaculture, in this article, five main aspects of aquaculture, i.e., life information acquisition, aquatic product growth regulation and decision-making, fish disease prediction and diagnosis, aquaculture environment perception and regulation, and aquaculture underwater robots, combined with the practical problems in aquaculture, were mainly focused on. The application principles and necessity of artificial intelligence technology in each aspect were explained. Commonly used technical methods were point out and the classic application cases were deeply analyzed. The main problems, bottlenecks and challenges in the current development of artificial intelligence technology in aquaculture were analyzed, including turbid water, multiple interference factors, corrosion of equipment, and movement of underwater animals, etc., and reasonable research directions for these potential challenges were pointed out. In addition, the main strategic strategies to promote the transformation of aquaculture were also proposed. The development of aquaculture is inseparable from artificial intelligence technology, this review can provide references to accelerate the advancement of digitalization, precision and intelligent aquaculture. 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 （3820）   HTML （353）    PDF（pc） （915KB）（3704）       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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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 （3488）   HTML （321）    PDF（pc） （2372KB）（3680）       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 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 （2034）   HTML （114）    PDF（pc） （1011KB）（3397）       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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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 （1648）   HTML （44）    PDF（pc） （800KB）（3360）       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）

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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 （1889）      PDF（pc） （2301KB）（3339）       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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Advances of Metatranscriptomics Technology Ma Shu, Liu Huhu, Tian Yun, Lu Xiangyang Biotechnology Bulletin    2012, 0 (12): 46-50.   Abstract （326）      PDF（pc） （1362KB）（3084）       Knowledge map    Save Metatranscriptomics is a new subject to study the transcription situation and regulation rules of entire genomes of colonialorganism under a certain specific environment and period in the overall level. The emergence, research strategies and current applications of theMetatranscriptomic were reviewed briefly and the application prospect of it was also anticipated in this paper. 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 （1139）   HTML （18）    PDF（pc） （933KB）（2974）       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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Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion DU JiaKuan, LI YanFei, SUN SiWen, LIU JiDong, JIANG TengDa Journal of Agricultural Big Data    2024, 6 (1): 56-67.   DOI: 10.19788/j.issn.2096-6369.000010 Abstract （786）   HTML （47）    PDF（pc） （9692KB）（2957）       Knowledge map    Save Traditional methods of rice phenological phase feature extraction based on time-series remote sensing images require high temporal resolution, which is difficult to meet due to imaging conditions. Due to the different environmental conditions in different rice growing regions, the rice planting area extraction method based on single image has poor generalization ability. In this paper, similar optical and Synthetic Aperture Radar (SAR) data were selected to reduce the spatiotemporal information differences in rice planting area images. The spatial feature information of optical data and backscatter information of SAR data were effectively used to extract rice features by using a two-structure network model through pan-spatio-temporal feature fusion. Experiments show that the overall test accuracy of the training model validation set on the rice datasets of Sanjiang Plain and Feixi County is 95.66%, and the Kappa coefficient is 0.8805. The results of rice extraction in Nanchang City were in good agreement with the actual field boundaries, and the overall extraction accuracy was 86.78%, which proved the generalization ability and practicability of the pan-temporal feature model. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Progress on the Studies and Application of Clostridium butyricum Zhang Shanting Shi Yan Zhang Shuli Wang Haikuan Biotechnology Bulletin    2013, 0 (9): 27-33.   Abstract （723）      PDF（pc） （1194KB）（2900）       Knowledge map    Save Clostridium butyricum, as a probiotic has been studied widely, is a spore-forming, gram-positive anaerobe that is found in intestines of healthy animals and humans. As a novel spore-forming probiotic, C. butyricum can withstand the low pH, heat and many antibiotic compared with non-spore forming probiotics, and exert different health effects in host, such as regulation of intestinal microbial homeostasis, stabilization of the gastrointestinal barrier function, enzymatic activity inducing absorption and nutrition, immunomodulatory effects, improvement of the animal production performance. So it will be used more widely. In this paper, we reviewed advances in the biological function, the fermentation technology and the application in clinic and feed additive of C. butyricum. Some advices were also put forward for further research of C. butyricum.Key words: Clostridium butyricum Probiotic Fermentation Clinic Feed additive 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 （1999）   HTML （64）    PDF（pc） （2358KB）（2860）       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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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 （2582）   HTML （109）    PDF（pc） （1052KB）（2830）       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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Application Research Progress of Proteomics Yin Wen, Fu Xu, Li Ping Biotechnology Bulletin    2014, 0 (1): 32-38.   Abstract （492）      PDF（pc） （1201KB）（2807）       Knowledge map    Save Proteomics is an emerging discipline for studying proteins composition and function in a type of cell, tissue or body fluids in a large-scale, high-throughput and systematic level. While genes determine the level of protein, but the level of gene expression can not represent the intracellular reactive protein levels. Proteomic analysis is a complement to the study of translation and modification and also an indispensable tool for a comprehensive understanding of genome expression. The development of proteomic technologies has greatly promoted the progress of proteomic research, and it has been widely used in various research fields.This paper revieweded the proteomic technologies and the applications in various fields are also briefly reviewed. Finally, some future issues are presented. Reference | Related Articles | Metrics | Comments（0）

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Sensitivity Analysis of Genetic Parameters of RiceGrow Model Yijun Meng, Xiaolei Qiu, Leilei Liu, Bing Liu, Yan Zhu, Weixing Cao, Liang Tang Journal of Agricultural Big Data    2021, 3 (3): 23-32.   DOI: 10.19788/j.issn.2096-6369.210303 Abstract （1784）   HTML （35）    PDF（pc） （1112KB）（2791）       Knowledge map    Save Genetic parameter calibration is an important step before applying the crop growth model, which often calls for a lot of time and effort. Sensitivity analysis can help to identify sensitive parameters, improve calibration efficiency, and simplify the model. Using Simlab and Matlab software, this study analyzed the sensitivity of rice genetic parameters of RiceGrow model by EFAST method and obtained the parameter sensitivity of the model in different regions and under different climate scenarios (historical meteorological data from 1981 to 2015 and global future warming 2.0℃ climate scenarios). The TDCC (Top-Downward-Coefficient of Concordance) coefficient was used to calculate the sensitivity ranking consistency. The results showed that Optimum Temperature (OT) was the most sensitive parameter affecting flowering period and total dry matter, followed by Temperature Sensitivity (TS), Photoperiod Sensitivity (PS) and Intrinsic Earliness (IE). OT was the most sensitive parameter affecting maturity period and the whole growth period. TS, IE, PS and Basic Filling Factor (BFF) were also sensitive parameters. The sensitive parameters affecting yield are mainly maximum CO2 assimilation rate (AMX), Specific Leaf Area (SLA) and Harvest Index (HI), followed by IE, TS, BFF, OT and PS. The sensitivity parameters in all regions and under different climate scenarios are relatively consistent, but the sensitivity ordering varies greatly. The sensitivity indexes of most parameters under warming climate scenarios slightly increase, while a few slightly decrease. The variation of parameter sensitivity under different climate scenarios is small, while which among different regions is large. When calibrating the model for phenology and dry matter, OT is the most sensitivity. In areas with low temperature and high latitude, the parameters related to temperature, photoperiod and photosynthesis should be focused. When calibrating the parameters of the yield, we need to focus on AMX, HI, SLA. Relative growth rate of LAI is not sensitive, so it can be ignored in parameter calibration, and can also be eliminated from the model to simplify the model. The results would be used to localize crop model and provide a way to improve the efficiency of parameter calibration. Table and Figures | Reference | Related Articles | Metrics | Comments（0）