[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4238":3,"related-4238":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":99},4238,"Comparative Performance of Machine Learning and Deep Learning Algorithms for Agricultural Land Mapping in Anambra State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106955","Abstract - Accurate delineation of agricultural land is fundamental to agricultural planning, food-security assessment, land-resource management, and monitoring of land-use transformation. However, the heterogeneous and fragmented character of agricultural landscapes in southeastern Nigeria presents substantial challenges for satellite-image classification. This study comparatively evaluated Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) for agricultural land mapping in Anambra State, Nigeria. Remotely sensed imagery was classified into waterbody, dense forest, agricultural land, built-up area, and bare ground using the four algorithms. Model performance was evaluated using 210 validation samples and multiple confusion-matrix metrics, including sensitivity, precision, specificity, negative predictive value, overall accuracy, balanced accuracy, and Matthew’s correlation coefficient. Agricultural land estimates varied from 1,000.26 km² (21.78%) for SVM to 1,099.12 km² (23.93%) for ANN. SVM achieved the strongest overall performance, recording 88.5% sensitivity, 85.2% precision, 75.0% specificity, 83.3% overall accuracy, 81.7% balanced accuracy, and a Matthews correlation coefficient of 0.643. CNN produced the lowest overall accuracy of 74.3%. The findings demonstrate that increased model complexity does not necessarily translate into superior classification performance and establish SVM as an effective approach for regional agricultural land delineation under heterogeneous tropical landscape conditions. Keywords: agricultural land mapping; machine learning; deep learning; Support Vector Machine; Random Forest; artificial neural network; convolutional neural network; remote sensing","摘要——准确划定农业用地是农业规划、粮食安全评估、土地资源管理和土地利用变化监测的基础。然而，尼日利亚东南部农业景观的异质性和破碎化特征给卫星图像分类带来了巨大挑战。本研究比较评估了随机森林（RF）、支持向量机（SVM）、人工神经网络（ANN）和卷积神经网络（CNN）在尼日利亚阿南布拉州农业用地制图中的表现。利用这四种算法将遥感影像分类为水体、密林、农业用地、建成区和裸地。使用210个验证样本和多种混淆矩阵指标评估模型性能，包括灵敏度、精确率、特异度、阴性预测值、总体精度、平衡精度和马修斯相关系数。农业用地估计值从SVM的1,000.26 km²（21.78%）到ANN的1,099.12 km²（23.93%）不等。SVM取得了最强的总体表现，灵敏度为88.5%，精确率为85.2%，特异度为75.0%，总体精度为83.3%，平衡精度为81.7%，马修斯相关系数为0.643。CNN的总体精度最低，为74.3%。研究结果表明，模型复杂度的增加并不必然带来更优的分类性能，并确立了SVM作为异质性热带景观条件下区域农业用地划定的有效方法。关键词：农业用地制图；机器学习；深度学习；支持向量机；随机森林；人工神经网络；卷积神经网络；遥感",null,"International Journal of Emerging Trends in Engineering and Development","2026-10-02T00:00:00Z","论文",25,false,61,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},8,20,16,9,1,"尼日利亚区域案例研究，方法对比扎实但影响范围限于学术圈，可作为遥感与农业AI主题的参考素材。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106954",[28,29,30,31,32],"智慧农业","农业人工智能","机器学习","遥感","农地制图",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,55,62,69,75],{"code":34,"score":47,"supported":48,"title":49,"abstract":52,"tags":54},27,true,[50,51],"machine learning","deep learning",[53,30,50,51],"深度学习",[29,30],{"code":56,"score":57,"supported":48,"title":58,"abstract":59,"tags":61},"I3",7,[],[31,60],"remote sensing",[31],{"code":63,"score":64,"supported":48,"title":65,"abstract":66,"tags":68},"A8",3,[],[67],"粮食安全",[],{"code":70,"score":64,"supported":48,"title":71,"abstract":72,"tags":74},"J1",[],[73],"农业规划",[],{"code":76,"score":21,"supported":14,"title":77,"abstract":78,"tags":79},"I1",[],[],[28],[81,82],"农业人工智能 农地制图 智慧农业 机器学习","农业人工智能 农地制图","农业人工智能农地制图智慧农业机器学习-4238",2,"10.5281\u002Fzenodo.23106955",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7219591984",[89],{"name":90,"orcid":9},"J.I. Igbokwe, C.E Ejikeme, J.O and Obi",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"比较RF、SVM、ANN、CNN在尼日利亚农业用地制图中的表现","遥感影像分类，210个验证样本，多种混淆矩阵指标评估","SVM总体精度最高（83.3%），CNN最低（74.3%），模型复杂度不保证性能","农业遥感与作物表型","在碎片化热带景观中，可探索轻量级集成学习或迁移学习以平衡精度与效率","openalex","2026-10-05T23:30:27.987196Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,165,259,309,427,483],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":9,"content":9,"source_name":108,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":128,"search_phrases":151,"slug":154,"view_count":143,"doi":9,"paper":155,"created_at":164},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241",10,78,{"impact":112,"substance":113,"depth":112,"authority":114,"freshness":101,"relevant":21,"comment":115},18,22,14,"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[117],{"name":108,"url":106},[28,29,30,31,119],"土壤监测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[122],{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":41,"locked":14,"version":42,"method":43,"evidence":129},{"candidates":130,"agricultural_evidence":48},[131,136,142,147],{"code":34,"score":19,"supported":48,"title":132,"abstract":134,"tags":135},[133],"artificial intelligence",[53,30],[29,30],{"code":123,"score":137,"supported":48,"title":138,"abstract":139,"tags":141},11,[124],[140],"土壤",[],{"code":56,"score":143,"supported":48,"title":144,"abstract":145,"tags":146},4,[],[31],[31],{"code":76,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[28],[152,153],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":156,"venue":9,"cited_by_count":91,"oa_url":9,"card":157,"direction":161,"ingested_from":163},[],{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","农业人工智能与决策模型","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":173,"score_detail":174,"sources":178,"tags":180,"primary_topic":182,"secondary_topics":183,"classification":193,"search_phrases":233,"slug":236,"view_count":143,"doi":237,"paper":238,"created_at":258},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",77,{"impact":112,"substance":18,"depth":175,"authority":176,"freshness":20,"relevant":21,"comment":177},17,13,"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。",[179],{"name":171,"url":168},[28,29,30,181,31],"可持续农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[184,189],{"code":185,"slug":186,"name":187,"path":188,"parent_code":127},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":56,"slug":190,"name":191,"path":192,"parent_code":38},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":41,"locked":14,"version":42,"method":43,"evidence":194},{"candidates":195,"agricultural_evidence":48},[196,200,205,209,216,223,229],{"code":34,"score":109,"supported":48,"title":197,"abstract":198,"tags":199},[50],[],[29,30],{"code":185,"score":20,"supported":48,"title":201,"abstract":203,"tags":204},[202],"sustainable agriculture",[],[181],{"code":56,"score":20,"supported":48,"title":206,"abstract":207,"tags":208},[60],[],[31],{"code":210,"score":101,"supported":48,"title":211,"abstract":212,"tags":215},"H7",[],[213,214],"气候变化","climate change",[],{"code":217,"score":101,"supported":48,"title":218,"abstract":219,"tags":222},"J3",[],[220,221],"全球农业","global agriculture",[],{"code":224,"score":64,"supported":48,"title":225,"abstract":226,"tags":228},"H6",[],[227],"农业生态",[],{"code":76,"score":21,"supported":14,"title":230,"abstract":231,"tags":232},[],[],[28],[234,235],"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":237,"openalex_id":239,"authors":240,"venue":171,"cited_by_count":91,"oa_url":168,"card":253,"direction":96,"ingested_from":98},"W7213301703",[241,244,247,250],{"name":242,"orcid":243},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":245,"orcid":246},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":248,"orcid":249},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":251,"orcid":252},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":254,"method":255,"finding":256,"direction":96,"opportunity":257},"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。","2026-09-16T23:30:28.640661Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":9,"content":9,"source_name":264,"source_url":9,"published_at":265,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":110,"score_detail":266,"sources":268,"tags":270,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":296,"slug":299,"view_count":21,"doi":300,"paper":301,"created_at":308},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",{"impact":112,"substance":113,"depth":112,"authority":114,"freshness":101,"relevant":21,"comment":267},"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。",[269],{"name":264,"url":262},[28,29,30,31,271],"产量估算",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[274],{"code":56,"slug":190,"name":191,"path":192,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":276},{"candidates":277,"agricultural_evidence":48},[278,282,286,292],{"code":34,"score":19,"supported":48,"title":279,"abstract":280,"tags":281},[30],[53,30],[29,30],{"code":56,"score":176,"supported":48,"title":283,"abstract":284,"tags":285},[31],[31],[271,31],{"code":287,"score":64,"supported":48,"title":288,"abstract":289,"tags":291},"I5",[],[290],"无线传感",[],{"code":76,"score":21,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[28],[297,298],"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":300,"openalex_id":9,"authors":302,"venue":9,"cited_by_count":91,"oa_url":9,"card":303,"direction":96,"ingested_from":163},[],{"tldr":304,"method":305,"finding":306,"direction":96,"opportunity":307},"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":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":317,"score_detail":318,"sources":321,"tags":323,"primary_topic":325,"secondary_topics":326,"classification":334,"search_phrases":406,"slug":409,"view_count":84,"doi":410,"paper":411,"created_at":426},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":112,"substance":113,"depth":112,"authority":319,"freshness":143,"relevant":21,"comment":320},12,"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。",[322],{"name":315,"url":312},[28,29,30,31,324],"园艺",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[327,328],{"code":56,"slug":190,"name":191,"path":192,"parent_code":38},{"code":329,"slug":330,"name":331,"path":332,"parent_code":333},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",{"status":41,"locked":14,"version":42,"method":43,"evidence":335},{"candidates":336,"agricultural_evidence":48},[337,342,349,355,366,370,377,384,390,396,402],{"code":34,"score":338,"supported":48,"title":339,"abstract":340,"tags":341},19,[50],[53,30,50],[29,30],{"code":56,"score":112,"supported":48,"title":343,"abstract":344,"tags":348},[60],[31,345,60,346,347],"无人机多光谱","multispectral","hyperspectral",[31],{"code":329,"score":20,"supported":48,"title":350,"abstract":351,"tags":354},[],[331,352,353],"草莓","strawberry",[],{"code":356,"score":20,"supported":48,"title":357,"abstract":358,"tags":365},"B2",[],[359,360,361,362,363,364],"果树","柑橘","芒果","荔枝","apple","citrus",[],{"code":123,"score":101,"supported":48,"title":367,"abstract":368,"tags":369},[],[140,124],[],{"code":371,"score":101,"supported":48,"title":372,"abstract":373,"tags":376},"I4",[],[374,375],"地理信息","GIS",[],{"code":378,"score":101,"supported":48,"title":379,"abstract":380,"tags":383},"I6",[],[381,382],"数字鸿沟","digital divide",[],{"code":385,"score":64,"supported":48,"title":386,"abstract":387,"tags":389},"B4",[],[388],"花卉",[],{"code":391,"score":64,"supported":48,"title":392,"abstract":393,"tags":395},"B6",[],[394],"药用植物",[],{"code":397,"score":64,"supported":48,"title":398,"abstract":399,"tags":401},"H3",[],[400],"水资源",[],{"code":76,"score":21,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[28],[407,408],"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":410,"openalex_id":412,"authors":413,"venue":315,"cited_by_count":91,"oa_url":419,"card":420,"direction":425,"ingested_from":98},"W7204829467",[414,417],{"name":415,"orcid":416},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384",{"name":418,"orcid":9},"Md Abdul Gaffor","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-AUG-2026-26.pdf",{"tldr":421,"method":422,"finding":423,"direction":96,"opportunity":424},"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":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":430,"published_at":434,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":317,"score_detail":435,"sources":437,"tags":439,"primary_topic":441,"secondary_topics":442,"classification":443,"search_phrases":467,"slug":470,"view_count":91,"doi":471,"paper":472,"created_at":482},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":319,"substance":113,"depth":112,"authority":319,"freshness":109,"relevant":21,"comment":436},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[438],{"name":433,"url":430},[28,29,67,31,440],"气候分析",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":444},{"candidates":445,"agricultural_evidence":48},[446,450,455,459,463],{"code":34,"score":20,"supported":48,"title":447,"abstract":448,"tags":449},[133],[],[29],{"code":287,"score":64,"supported":48,"title":451,"abstract":452,"tags":454},[],[453],"农业传感",[],{"code":63,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[67],{"code":76,"score":21,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[28],{"code":56,"score":21,"supported":14,"title":464,"abstract":465,"tags":466},[],[],[31],[468,469],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":471,"openalex_id":473,"authors":474,"venue":433,"cited_by_count":91,"oa_url":430,"card":477,"direction":161,"ingested_from":98},"W7223111766",[475],{"name":476,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":478,"method":479,"finding":480,"direction":96,"opportunity":481},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":484,"title":485,"url":486,"summary":487,"summary_zh":488,"content":9,"source_name":433,"source_url":486,"published_at":434,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":489,"score_detail":490,"sources":492,"tags":494,"primary_topic":497,"secondary_topics":498,"classification":500,"search_phrases":517,"slug":520,"view_count":91,"doi":521,"paper":522,"created_at":532},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。",67,{"impact":17,"substance":18,"depth":175,"authority":319,"freshness":109,"relevant":21,"comment":491},"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。",[493],{"name":433,"url":486},[28,29,495,31,496],"产量预测","可解释性",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[499],{"code":56,"slug":190,"name":191,"path":192,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":501},{"candidates":502,"agricultural_evidence":48},[503,509,513],{"code":34,"score":319,"supported":48,"title":504,"abstract":506,"tags":508},[505],"computer vision",[507],"计算机视觉",[29],{"code":56,"score":17,"supported":48,"title":510,"abstract":511,"tags":512},[],[31,60],[495,31],{"code":76,"score":21,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[28],[518,519],"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":521,"openalex_id":523,"authors":524,"venue":433,"cited_by_count":91,"oa_url":486,"card":527,"direction":96,"ingested_from":98},"W7223114002",[525],{"name":526,"orcid":9},"Kadiatou Barry",{"tldr":528,"method":529,"finding":530,"direction":96,"opportunity":531},"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"]