[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2272":3,"related-2272":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":42,"search_phrases":70,"slug":73,"view_count":74,"doi":8,"paper":75,"created_at":85},2272,"MDPI Remote Sensing 18(18):3107 MODIS NDVI与机器学习作物产量预测比较研究","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3107","Arai与Sanwal在MDPI Remote Sensing发表研究,基于印度2000-2026年州级数据,随机森林在保留时间顺序的走前验证中MAPE=11.4%、R²=0.982,优于梯度提升(MAPE=13.0%)。MODIS NDVI年最大值与粮食产量相关性r≈0.84,提供了可扩展的食品安全评估与农业决策支持实用路线。",null,"MDPI Remote Sensing 18(18):3107","2026-09-09T16:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,6,1,"基于印度2000-2026年州级数据的MODIS NDVI与机器学习产量预测对比研究，方法严谨、结论可靠，对农业遥感估产具有参考价值，但属境外案例、非国内政策或产业突破，适合作为专业精选而非头条。",[23],{"name":9,"url":6},[25,26,27,28,29],"农业人工智能","粮食安全","机器学习","遥感","作物产量预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":13,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":49},[48,54,58,65],{"code":31,"score":18,"supported":49,"title":50,"abstract":52,"tags":53},true,[51],"remote sensing",[51],[29,28],{"code":38,"score":12,"supported":49,"title":55,"abstract":56,"tags":57},[27],[],[25,27],{"code":59,"score":60,"supported":49,"title":61,"abstract":62,"tags":64},"E4",3,[],[63],"食品安全",[],{"code":66,"score":20,"supported":13,"title":67,"abstract":68,"tags":69},"A8",[],[],[26],[71,72],"作物产量预测 农业人工智能 机器学习 粮食安全","作物产量预测 农业人工智能","作物产量预测农业人工智能机器学习粮食安全-2272",4,{"doi":8,"openalex_id":8,"authors":76,"venue":8,"cited_by_count":77,"oa_url":8,"card":78,"direction":82,"ingested_from":84},[],0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"基于印度州级MODIS NDVI与机器学习比较作物产量预测，随机森林优于梯度提升。","用2000-2026年印度州级MODIS NDVI年最大值与随机森林、梯度提升走","随机森林MAPE=11.4%、R²=0.982，NDVI与产量相关性r≈0.84。","农业遥感与作物表型","可在中国等区域验证NDVI-产量模型迁移性，并融合多源遥感与气象提升预测鲁棒性。","agent","2026-09-13T00:04:05.087805Z",{"total":19,"page":20,"page_size":19,"items":87},[88,184,238,337,452,505],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":8,"source_name":94,"source_url":91,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":96,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":107,"classification":115,"search_phrases":157,"slug":160,"view_count":74,"doi":161,"paper":162,"created_at":183},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",{"impact":16,"substance":97,"depth":98,"authority":18,"freshness":99,"relevant":20,"comment":100},20,17,9,"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[102],{"name":94,"url":91},[104,25,27,105,28],"智慧农业","可持续农业",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[108,114],{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":116},{"candidates":117,"agricultural_evidence":49},[118,123,128,132,139,146,152],{"code":38,"score":12,"supported":49,"title":119,"abstract":121,"tags":122},[120],"machine learning",[],[25,27],{"code":109,"score":99,"supported":49,"title":124,"abstract":126,"tags":127},[125],"sustainable agriculture",[],[105],{"code":31,"score":99,"supported":49,"title":129,"abstract":130,"tags":131},[51],[],[28],{"code":133,"score":19,"supported":49,"title":134,"abstract":135,"tags":138},"H7",[],[136,137],"气候变化","climate change",[],{"code":140,"score":19,"supported":49,"title":141,"abstract":142,"tags":145},"J3",[],[143,144],"全球农业","global agriculture",[],{"code":147,"score":60,"supported":49,"title":148,"abstract":149,"tags":151},"H6",[],[150],"农业生态",[],{"code":153,"score":20,"supported":13,"title":154,"abstract":155,"tags":156},"I1",[],[],[104],[158,159],"农业人工智能 可持续农业 智慧农业 机器学习","农业人工智能 可持续农业","农业人工智能可持续农业智慧农业机器学习-2659","10.3390\u002Fsu18189472",{"doi":161,"openalex_id":163,"authors":164,"venue":94,"cited_by_count":77,"oa_url":91,"card":177,"direction":82,"ingested_from":182},"W7213301703",[165,168,171,174],{"name":166,"orcid":167},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":169,"orcid":170},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":172,"orcid":173},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":175,"orcid":176},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":178,"method":179,"finding":180,"direction":82,"opportunity":181},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","openalex","2026-09-16T23:30:28.640661Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":8,"content":8,"source_name":189,"source_url":8,"published_at":190,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":191,"score_detail":192,"sources":195,"tags":197,"primary_topic":199,"secondary_topics":200,"classification":202,"search_phrases":225,"slug":228,"view_count":20,"doi":229,"paper":230,"created_at":237},2212,"《基于遥感的农作物产量估算综述:机器学习技术及环境、算法和硬件限制》","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffpls.2026.1742689\u002Ffull","Muhammad等系统综述遥感技术在农作物和植物产量估算中的应用进展,提出将遥感方法学系统分类为:传感器方法、平台方法、分析与建模方法、机器学习方法。基于多项研究结果发现,基于深度学习的架构在精度、查准率、查全率和F1分数等关键评估指标上一致实现优越性能,这种性能优势源于其学习分层表示、捕捉复杂非线性关系、高效扩展大规模数据集的能力。论文还将局限性系统归纳为环境、算法、硬件操作和无线传感器网络四大类别。","Frontiers in Plant Science 2026年9月","2026-09-07T00:00:00Z",78,{"impact":16,"substance":17,"depth":16,"authority":193,"freshness":19,"relevant":20,"comment":194},14,"系统综述遥感产量估算方法学并归纳环境、算法与硬件限制，方法分类清晰、结论可靠，对农业遥感与AI应用有较高参考价值。",[196],{"name":189,"url":187},[104,25,27,28,198],"产量估算",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[201],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":203},{"candidates":204,"agricultural_evidence":49},[205,211,215,221],{"code":38,"score":206,"supported":49,"title":207,"abstract":208,"tags":210},16,[27],[209,27],"深度学习",[25,27],{"code":31,"score":18,"supported":49,"title":212,"abstract":213,"tags":214},[28],[28],[198,28],{"code":216,"score":60,"supported":49,"title":217,"abstract":218,"tags":220},"I5",[],[219],"无线传感",[],{"code":153,"score":20,"supported":13,"title":222,"abstract":223,"tags":224},[],[],[104],[226,227],"农业人工智能 产量估算 智慧农业 机器学习","农业人工智能 产量估算","农业人工智能产量估算智慧农业机器学习-2212","10.3389\u002Ffpls.2026.1742689\u002Ffull",{"doi":229,"openalex_id":8,"authors":231,"venue":8,"cited_by_count":77,"oa_url":8,"card":232,"direction":82,"ingested_from":84},[],{"tldr":233,"method":234,"finding":235,"direction":82,"opportunity":236},"系统综述遥感估产方法，分类传感器、平台、建模与机器学习，指出深度学习性能最优。","文献综述，按传感器、平台、分析建模、机器学习四类归纳遥感估产方法。","深度学习在精度、查准率、召回率和F1上一致优于其他方法，局限归为环境、算法、硬件与无线传感器网络四类","可针对综述指出的环境与硬件限制，研究轻量化深度学习模型在边缘设备上的实时估产。","2026-09-12T00:06:40.290409Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":8,"source_name":244,"source_url":241,"published_at":245,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":246,"score_detail":247,"sources":251,"tags":253,"primary_topic":255,"secondary_topics":256,"classification":263,"search_phrases":312,"slug":314,"view_count":315,"doi":316,"paper":317,"created_at":336},2160,"An Optimized Temporal Attention-based Deep Learning Framwork for Accurate Crop Yield Prediction from Multi-source Agricultural Data","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fag.d-6558","Background: In precision agriculture, crop yield prediction is crucial for supporting effective management of crops, timely settlement of farmers crop insurances, food security planning and serve as decision tool for sustainable agricultural achievements. However, prediction of crop yield is difficult due to the complex interaction behavior among diverse conditions in weather, soil properties, multispectral remote sensing observations and the dynamics in crop growth. Despite existing AI techniques like machine learning and deep learning models outperformed in predicting crop yield, they are often not able to capture the temporal dependencies. Furthermore, determining optimal hyperparameters for model training remains a challenging task, significantly influencing prediction accuracy, model convergence and generalization performance. Methods: To overcome this, the present study aimed to predict the crop yield using temporal attention (TA) based Artificial bee colony (ABC) optimized Bi - LSTM model using multi source agricultural data. The proposed model learns the sequential and temporal dependencies from both past and future time steps simultaneously, from the historical weather variables, soil properties, remote sensing derived normalized difference vegetation indices (NDVI) and historical yield data. The ABC algorithm was employed to automatically optimize the model hyperparameters with minimum run time. Result: The model is trained using district wise rice yield records of Tamil Nadu, historical weather variables, Sentinel-2 derived NDVI and soil properties. The dataset collected for major rice growing districts from 1995 to 2025 (30 years). The model shows the superior performance over conventional methods like random forest, support vector regression, deep neural network, convolutional neural network, long short-term memory models with lesser root mean square error (RMSE = 298.01 kg ha-1) and higher coefficient of determination, R2 (81.76%). Also, the training time of TA - ABC - Bi-LSTM model is comparatively less than the comparative models because of ABC optimization algorithm tuned the best hyperparameters compared to traditional trial and error manual method.","背景：在精准农业中，作物产量预测对于支持有效的作物管理、农民作物保险的及时理赔、粮食安全规划以及作为实现可持续农业的决策工具至关重要。然而，由于天气、土壤属性、多光谱遥感观测等多种条件之间复杂的交互作用以及作物生长的动态变化，作物产量预测仍然困难。尽管现有的机器学习、深度学习等人工智能技术在作物产量预测方面表现优异，但往往无法捕捉时间依赖性。此外，确定模型训练的最优超参数仍是一项具有挑战性的任务，显著影响预测精度、模型收敛性和泛化性能。方法：为解决上述问题，本研究旨在利用基于时间注意力（TA）的人工蜂群（ABC）算法优化的双向长短期记忆（Bi-LSTM）模型，结合多源农业数据预测作物产量。所提出的模型能够同时从历史和未来时间步中学习序列和时间依赖关系，数据来源包括历史气象变量、土壤属性、遥感衍生的归一化植被指数（NDVI）以及历史产量数据。采用ABC算法以最短运行时间自动优化模型超参数。结果：该模型使用泰米尔纳德邦各地区的稻米产量记录、历史气象变量、Sentinel-2衍生的NDVI和土壤属性进行训练。数据集收集了1995年至2025年（30年）主要水稻种植区的数据。该模型相比随机森林、支持向量回归、深度神经网络、卷积神经网络、长短期记忆模型等传统方法表现出更优的性能，均方根误差更低（RMSE = 298.01 kg ha-1），决定系数R²更高（81.76%）。此外，TA-ABC-Bi-LSTM模型的训练时间相比对比模型更短，因为ABC优化算法相比传统的试错手动方法能够调优出最佳超参数。","Agricultural Science Digest - A Research Journal","2026-09-10T00:00:00Z",76,{"impact":206,"substance":248,"depth":16,"authority":249,"freshness":99,"relevant":20,"comment":250},21,12,"提出时序注意力结合人工蜂群优化的Bi-LSTM产量预测模型，基于30年多源数据取得较高精度，方法新颖且数据规模可观，对精准农业与农业保险决策有参考价值。",[252],{"name":244,"url":241},[104,25,254,28,29],"水稻",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[257,258],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":259,"slug":260,"name":261,"path":262,"parent_code":35},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":43,"locked":13,"version":44,"method":45,"evidence":264},{"candidates":265,"agricultural_evidence":49},[266,271,277,282,289,296,301,308],{"code":38,"score":16,"supported":49,"title":267,"abstract":269,"tags":270},[268],"deep learning",[209,27,120,268],[25],{"code":31,"score":272,"supported":49,"title":273,"abstract":274,"tags":276},11,[],[28,51,275],"multispectral",[29,28],{"code":259,"score":272,"supported":49,"title":278,"abstract":280,"tags":281},[279],"agricultural data",[279],[],{"code":283,"score":12,"supported":49,"title":284,"abstract":285,"tags":288},"A1",[],[254,286,287],"稻米","rice",[254],{"code":153,"score":290,"supported":49,"title":291,"abstract":292,"tags":295},7,[],[293,294],"精准农业","precision agriculture",[104],{"code":66,"score":19,"supported":49,"title":297,"abstract":298,"tags":300},[],[26,299],"food security",[],{"code":302,"score":19,"supported":49,"title":303,"abstract":304,"tags":307},"H2",[],[305,306],"土壤","soil",[],{"code":109,"score":60,"supported":49,"title":309,"abstract":310,"tags":311},[],[105],[],[313,72],"作物产量预测 农业人工智能 智慧农业 水稻","作物产量预测农业人工智能智慧农业水稻-2160",2,"10.18805\u002Fag.d-6558",{"doi":316,"openalex_id":318,"authors":319,"venue":244,"cited_by_count":77,"oa_url":241,"card":330,"direction":82,"ingested_from":182},"W7212119230",[320,322,324,326,328],{"name":321,"orcid":8},"M. Lokeshwari",{"name":323,"orcid":8},"Nivethitha Manavalagan",{"name":325,"orcid":8},"P. Amrutha",{"name":327,"orcid":8},"S. Harinee",{"name":329,"orcid":8},"N.R. Divyasree",{"tldr":331,"method":332,"finding":333,"direction":334,"opportunity":335},"提出TA-ABC-Bi-LSTM模型，用多源农业数据精准预测水稻产量。","基于时间注意力与人工蜂群优化的Bi-LSTM，融合气象、土壤、Sentinel-","模型RMSE为298.01 kg\u002Fha，R²达81.76%，优于RF、SVR、DNN、CNN、LST","农业人工智能与决策模型","可探索轻量化注意力机制与迁移学习，将模型推广至多作物、小样本及实时预测场景。","2026-09-11T23:30:29.551995Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":8,"source_name":343,"source_url":340,"published_at":344,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":352,"secondary_topics":353,"classification":361,"search_phrases":431,"slug":434,"view_count":315,"doi":435,"paper":436,"created_at":451},1299,"Remote Sensing and Machine Learning for Futuristic Horticulture: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.25125\u002Fijoear-aug-2026-26","Horticultural crops—fruits, vegetables, plantation crops, flowers, spices and medicinal plants—form the nutritional and economic backbone of India's agricultural sector. However, they face persistent challenges of yield variability, water and nutrient inefficiency, pest and disease pressure and post-harvest losses. This review synthesises the transformative role of Remote Sensing (RS) and Machine Learning (ML) in enabling futuristic, sustainable and precision horticulture. The evolution of agricultural production systems from Agriculture 1.0 to Agriculture 5.0 is traced, followed by a comprehensive discussion of RS principles, elements, four fundamental resolutions (spatial, spectral, temporal, radiometric), and multi-platform architectures (satellite, aerial, UAV, ground-based). A parallel taxonomy of ML—supervised, semi-supervised, unsupervised, reinforcement and deep-learning frameworks—is presented, with emphasis on Random Forest, Support Vector Machine, XGBoost, and Convolutional Neural Network algorithms most widely applied to horticultural datasets. Five representative case studies are analysed in depth: (i) UAV multispectral + ensemble learning for leaf nitrogen monitoring in custard apple; (ii) hyperspectral + ANN \u002F RF \u002F DT models for mango and strawberry fruit-quality parameters; (iii) UAV-hyperspectral detection of citrus canker; (iv) machine-learning-based crop recommendation under NPK, pH and climatic variables; and (v) RS + GIS soil-site suitability for six major fruit crops in the Ganjigatti sub-watershed, Karnataka. National platforms such as CHAMAN, Bhoonidhi, Bhuvan and PMFBY are reviewed as operational demonstrations of the RS + ML value chain. Finally, technical, economic and ethical challenges—ground-truth scarcity, sensor cost, model interpretability, digital divide and data privacy—are discussed, and a way forward for Indian horticulture in the Agriculture 5.0 era is proposed.","园艺作物——包括水果、蔬菜、种植园作物、花卉、香料及药用植物——构成了印度农业部门营养与经济的基础。然而，它们持续面临产量波动、水资源与养分利用效率低下、病虫害压力以及采后损失等挑战。本综述综合探讨了遥感（Remote Sensing, RS）与机器学习（Machine Learning, ML）在推动未来化、可持续及精准园艺中的变革性作用。文章追溯了农业生产系统从农业1.0到农业5.0的演进历程，随后全面讨论了遥感的基本原理、要素、四种基本分辨率（空间、光谱、时间、辐射）以及多平台架构（卫星、航空、无人机、地面）。同时，提出了机器学习的并行分类体系——监督学习、半监督学习、无监督学习、强化学习及深度学习框架，并重点介绍了最广泛应用于园艺数据集的随机森林、支持向量机、XGBoost和卷积神经网络算法。文章深入分析了五个代表性案例研究：（i）基于无人机多光谱与集成学习对番荔枝叶片氮素监测；（ii）基于高光谱与人工神经网络\u002F随机森林\u002F决策树模型对芒果和草莓果实品质参数的评估；（iii）基于无人机高光谱的柑橘溃疡病检测；（iv）基于NPK、pH及气候变量的机器学习作物推荐系统；以及（v）基于遥感和地理信息系统（GIS）的卡纳塔克邦甘吉加蒂子流域六种主要果树土壤-立地适宜性评价。文章还回顾了CHAMAN、Bhoonidhi、Bhuvan和PMFBY等国家平台，作为遥感与机器学习价值链的运营示范。最后，讨论了技术、经济和伦理方面的挑战——包括地面真值稀缺、传感器成本、模型可解释性、数字鸿沟和数据隐私问题，并提出了印度园艺业在农业5.0时代的发展路径。","International Journal of Environmental and Agriculture Research","2026-08-31T00:00:00Z",74,{"impact":16,"substance":17,"depth":16,"authority":249,"freshness":74,"relevant":20,"comment":347},"综述全面，案例详实，对印度园艺精准农业有参考价值，但时效性一般。",[349],{"name":343,"url":340},[104,25,27,28,351],"园艺",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[354,355],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":356,"slug":357,"name":358,"path":359,"parent_code":360},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",{"status":43,"locked":13,"version":44,"method":45,"evidence":362},{"candidates":363,"agricultural_evidence":49},[364,369,375,381,392,396,403,409,415,421,427],{"code":38,"score":365,"supported":49,"title":366,"abstract":367,"tags":368},19,[120],[209,27,120],[25,27],{"code":31,"score":16,"supported":49,"title":370,"abstract":371,"tags":374},[51],[28,372,51,275,373],"无人机多光谱","hyperspectral",[28],{"code":356,"score":99,"supported":49,"title":376,"abstract":377,"tags":380},[],[358,378,379],"草莓","strawberry",[],{"code":382,"score":99,"supported":49,"title":383,"abstract":384,"tags":391},"B2",[],[385,386,387,388,389,390],"果树","柑橘","芒果","荔枝","apple","citrus",[],{"code":302,"score":19,"supported":49,"title":393,"abstract":394,"tags":395},[],[305,306],[],{"code":397,"score":19,"supported":49,"title":398,"abstract":399,"tags":402},"I4",[],[400,401],"地理信息","GIS",[],{"code":259,"score":19,"supported":49,"title":404,"abstract":405,"tags":408},[],[406,407],"数字鸿沟","digital divide",[],{"code":410,"score":60,"supported":49,"title":411,"abstract":412,"tags":414},"B4",[],[413],"花卉",[],{"code":416,"score":60,"supported":49,"title":417,"abstract":418,"tags":420},"B6",[],[419],"药用植物",[],{"code":422,"score":60,"supported":49,"title":423,"abstract":424,"tags":426},"H3",[],[425],"水资源",[],{"code":153,"score":20,"supported":13,"title":428,"abstract":429,"tags":430},[],[],[104],[432,433],"农业人工智能 智慧农业 机器学习 园艺","农业人工智能 智慧农业","农业人工智能智慧农业机器学习园艺-1299","10.25125\u002Fijoear-aug-2026-26",{"doi":435,"openalex_id":437,"authors":438,"venue":343,"cited_by_count":77,"oa_url":444,"card":445,"direction":450,"ingested_from":182},"W7204829467",[439,442],{"name":440,"orcid":441},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384",{"name":443,"orcid":8},"Md Abdul Gaffor","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-AUG-2026-26.pdf",{"tldr":446,"method":447,"finding":448,"direction":82,"opportunity":449},"综述遥感与机器学习在印度园艺作物精准管理中的应用，涵盖原理、方法、案例与挑战。","系统综述遥感平台与机器学习算法，分析五个典型案例及国家平台。","遥感与机器学习结合可提升园艺作物养分、病虫害、适种性等管理精度，但面临成本与数据挑战。","可探索低成本、可解释的遥感-机器学习模型，并集成多源数据以解决小农园艺场景的适用性问题。","智慧农业 \u002F 农业物联网","2026-09-01T23:30:09.105185Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":8,"source_name":458,"source_url":455,"published_at":459,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":460,"score_detail":461,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":470,"classification":472,"search_phrases":489,"slug":492,"view_count":77,"doi":493,"paper":494,"created_at":504},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",67,{"impact":462,"substance":97,"depth":98,"authority":249,"freshness":12,"relevant":20,"comment":463},8,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[465],{"name":458,"url":455},[104,25,467,28,468],"产量预测","可解释性",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[471],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":473},{"candidates":474,"agricultural_evidence":49},[475,481,485],{"code":38,"score":249,"supported":49,"title":476,"abstract":478,"tags":480},[477],"computer vision",[479],"计算机视觉",[25],{"code":31,"score":462,"supported":49,"title":482,"abstract":483,"tags":484},[],[28,51],[467,28],{"code":153,"score":20,"supported":13,"title":486,"abstract":487,"tags":488},[],[],[104],[490,491],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":493,"openalex_id":495,"authors":496,"venue":458,"cited_by_count":77,"oa_url":455,"card":499,"direction":82,"ingested_from":182},"W7223114002",[497],{"name":498,"orcid":8},"Kadiatou Barry",{"tldr":500,"method":501,"finding":502,"direction":82,"opportunity":503},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":8,"source_name":511,"source_url":508,"published_at":512,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":513,"score_detail":514,"sources":516,"tags":518,"primary_topic":522,"secondary_topics":525,"classification":528,"search_phrases":543,"slug":546,"view_count":77,"doi":547,"paper":548,"created_at":588},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":249,"substance":97,"depth":206,"authority":18,"freshness":462,"relevant":20,"comment":515},"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[517],{"name":511,"url":508},[27,28,519,520,521],"土壤碳","Landsat","数字土壤制图",{"code":302,"slug":306,"name":523,"path":524,"parent_code":113},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[526,527],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":529},{"candidates":530,"agricultural_evidence":49},[531,535,539],{"code":302,"score":193,"supported":49,"title":532,"abstract":533,"tags":534},[306],[305,306],[],{"code":38,"score":99,"supported":49,"title":536,"abstract":537,"tags":538},[120],[],[27],{"code":31,"score":462,"supported":49,"title":540,"abstract":541,"tags":542},[],[28,51],[520,28],[544,545],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":547,"openalex_id":549,"authors":550,"venue":511,"cited_by_count":77,"oa_url":8,"card":583,"direction":82,"ingested_from":182},"W7222107492",[551,554,557,560,563,566,569,572,574,577,580],{"name":552,"orcid":553},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":555,"orcid":556},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":558,"orcid":559},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":561,"orcid":562},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":564,"orcid":565},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":567,"orcid":568},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":570,"orcid":571},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":573,"orcid":8},"A. E. Shatunov",{"name":575,"orcid":576},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":578,"orcid":579},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":581,"orcid":582},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":584,"method":585,"finding":586,"direction":82,"opportunity":587},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z"]