农业大数据研究进展(2024)

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第一联系人:

张学福,E-mail:zhangxuefu@caas.cn

发布日期:2024-10-26

网络出版日期: 2024-12-02

基金资助

三亚中国农业科学院国家南繁研究院南繁专项课题:科技自立自强背景下国家南繁研究院种业科技创新发展路径研究

Progress of Agricultural Big Data Research (2024)

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Online published: 2024-12-02

本文引用格式

中国农业科学院农业信息研究所 . 农业大数据研究进展(2024)[J]. 农业大数据学报, 2024 , 6(4) : 433 -468 . DOI: 10.19788/j.issn.2096-6369.200003

参考文献

[1] MANYIKA J, CHUI M, BROWN B, et al. Big data: The next frontier for innovation, competition, and productivity. 2011.
[2] WOLFERT S, GE L, VERDOUW C, et al. Big Data in Smart Farming-A review[J]. Agricultural Systems, 2017, 153:69-80. DOI:10.1016/j.agsy.2017.01.023.
[3] El-KISHKY A, SONG Y L, WANG C, et al. Scalable Topical Phrase Mining from Text Corpora[J]. Proceedings of the VLDB Endowment, 2014, 8(3): 305-316.
[4] KAMILARIS A, KARTAKOULLIS A, PRENAFETA-BOLDú F. A review on the practice of big data analysis in agriculture[J]. Computers and Electronics in Agriculture, 2017, 143:23-37. DOI:10.1016/j.compag.2017.09.037.
[5] MAYAGOPAL P S, CHINTALA B. Big Data Challenges and Opportunities in Agriculture[J]. International Journal of Agricultural and Environmental Information Systems, 2020, 11:48-66. DOI:10.4018/ijaeis.2020010103.
[6] SHARMA R P, RAMESH D, PAL P, et al. IoT enabled IEEE 802.15.4 WSN monitoring infrastructure driven Fuzzy-logic based Crop pest prediction[J]. IEEE Internet of Things Journal, 2021,(99):1-1. DOI:10.1109/JIOT.2021.3094198.
[7] CHEN W L, LIN Y B, NG F L, et al. RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies[J]. IEEE Internet of Things Journal, 2020, 7(2):1001-1010. DOI:10.1109/JIOT.2019.2947624.
[8] MYSTKOWSKI A, WOLNIAKOWSKI A, IDZKOWSKI A, et al. Measurement and diagnostic system for detecting and classifying faults in the rotary hay tedder using multilayer perceptron neural networks[J]. Engineering Applications of Artificial Intelligence, 2024, 133. DOI:10.1016/j.engappai.2024.108513.
[9] D'ANDRIMONT R, CLAVERIE M, KEMPENEERS P, et al. AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography[J]. Earth System Science Data.15, 2023(1):317-329. DOI:10.5194/essd-15-317-2023.
[10] PATRIK A, UTAMA G, GUNAWAN A A S, et al. GNSS-based navigation systems of autonomous drone for delivering items[J]. Journal of Big Data, 2019, 6(1). DOI:10.1186/s40537-019-0214-3.
[11] JANNEH L L, ZHANG Y, CUI Z, et al. Multi-level feature re-weighted fusion for the semantic segmentation of crops and weeds[J]. Journal of King Saud University - Computer and Information Sciences. 2023, 35(6):101545. DOI:10.1016/j.jksuci.2023.03.023.
[12] CHAVES M E D, PICOLI M C A, SANCHES I. Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review[J]. Remote Sensing, 2020, 12:3062. DOI:10.3390/rs12183062.
[13] ACOSTA-ALBA I, CHIA E, ANDRIEU N. The LCA4CSA framework: Using life cycle assessment to strengthen environmental sustainability analysis of climate smart agriculture options at farm and crop system levels[J]. Agricultural Systems, 2019, 171:155-170. DOI:10.1016/j.agsy.2019.02.001.
[14] MIAO B-B, DONG W, GU Y-X. OmicsSuite:a customized and pipelined suite for analysis and visualization of multi-omics big data[J]. 2023, 10(11):69-82. DOI:10.1093/hr/uhad195.
[15] HUANG M, LIU X, ZHOU Y, et al. BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions[J]. GigaScience, 2018. DOI:10.1093/gigascience/giy154.
[16] NEETHIRAJAN S, KEMP B. Digital Phenotyping in Livestock Farming[J]. Animals (Basel), 2021, 75;11(7):2009. DOI: 10.3390/ani11072009.
[17] JOSHI A, GUEVARA D, EARLES M. Standardizing and centralizing datasets for efficient training of agricultural deep learning models[J]. Plant Phenomics, 2023, 5:0084. DOI:10.34133/plantphenomics.0084.
[18] COTTER M, ASCH F, ABERA B B, et al. Creating the data basis to adapt agricultural decision support tools to new environments, land management and climate change—A case study of the RiceAdvice App[J]. Journal of Agronomy and Crop Science, 2020, 206(4):423-432. DOI:10.1111/jac.12421.
[19] LAMBERT J P T, CHILDS D Z, FRECKLETON R P. Testing the ability of Unmanned Aerial Systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds)[J]. Pest Management Science, 2019. DOI:10.1002/ps.5444.
[20] ALLARD D W, HENDRIK B, DAVIDE F, et al. 25 years of the WOFOST cropping systems model[J]. Agricultural Systems, 2018, 168: S0308521X17310107. DOI:10.1016/j.agsy.2018.06.018.
[21] LEAL FILHO W, WALL T, RUI MUCOVA S A, et al. Deploying artificial intelligence for climate change adaptation[J]. Technological Forecasting and Social Change, 2022, 180.
[22] HURTT G C, CHINI L, SAHAJPAL R, et al. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6[J]. Geoscientific Model Development, 2020(13):5425-5464. DOI:10.5194/gmd-13-5425-2020.
[23] DePAIVA, Isaias Isaias, AntonioRita, Yohanne Larissa Cavalieri-Polizeli, Karina Maria. Knowledge and use of visual soil structure assessment methods in Brazil - A survey[J]. Soil & Tillage Research, 2020, 204:104704. DOI:10.1016/j.still.2020.104704.
[24] MAHAFFEE W F. Catching Spores: Linking Epidemiology, Pathogen Biology, and Physics to Ground-Based Airborne Inoculum Monitoring[J]. Plant Disease, 2023, 107(1):13-33. DOI:10.1094/PDIS-11-21-2570-FE.
[25] HARFOUCHE A L, JACOBSON D A, KAINER D, et al. Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence[J]. Trends in Biotechnology, 2019, 37(11):1217-1235. DOI:10.1016/j.tibtech.2019.05.007.
[26] ZHU C, YU H, LI J Q. Deep learning‐based association analysis of root image data and cucumber yield[J]. The Plant Journal, 2024, 118(3):696-716. DOI:10.1111/tpj.16627.
[27] PINOSIO S, MARRONI F, ZUCCOLO A, et al. A draft genome of sweet cherry (Prunus avium L.) reveals genome-wide and local effects of domestication[J]. The Plant Journal, 2020, 103: 1420-1432. DOI:10.1111/tpj.14809
[28] LOPEZ-CRUZ M, AGUATE F M, WASHBURN J D, et al. Leveraging data from the genomes-to-fields initiative to investigate genotype-by-environment interactions in maize in north america[J]. Nature Communications, 2023, 14(1), 6904. DOI:10.1038/s41467-023-42687-4.
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