[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3522":3,"related-3522":129},{"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":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":94,"slug":97,"view_count":21,"doi":98,"paper":99,"created_at":128},3522,"An agronomically informed framework for temporally transferable yield prediction","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10454-2","Abstract Purpose Yield maps are essential for agricultural decision-making, yet existing methods are often crop- or machine-specific, limiting their broader applicability. Methods This study introduces an agronomically informed framework for temporally transferable yield prediction that combines optimal phenological moment (OPM) selection with a spatial yield redistribution strategy: density-based yield mapping (DBYM). The framework relies on vegetation indices derived from satellite imagery acquired at physiologically meaningful crop stages and on independently known field-scale production values to guide spatial yield allocation. The method was evaluated across 10 growing seasons involving coffee, sugarcane, and wheat under both manual and mechanized production systems in commercial fields located in Brazil and Australia. In addition to DBYM, a simple linear regression baseline using the same OPM-derived inputs was evaluated to investigate the contribution of agronomically informed temporal input selection itself. Results Results demonstrated that OPM-based inputs alone already provided strong predictive capability across seasons, while DBYM improved the spatial coherence of predictions, particularly under sparse calibration scenarios. The index of agreement ranged from 0.73 to 0.94, while Moran’s bivariate index was significant ( p \u003C 0.01) for all DBYM predictions, confirming spatial consistency between observed and predicted yield patterns. RMSE values were comparable to those reported in machine learning and deep learning yield prediction studies, ranging from 0.21 to 0.28 Mg ha⁻¹ for coffee, 5.43–6.22 Mg ha⁻¹ for sugarcane, 0.15–0.26 Mg ha⁻¹ for wheat in Brazil, and 0.44–0.61 Mg ha⁻¹ for wheat in Australia. Conclusion The results suggest that predictive performance can be affected by the agronomic relevance of acquisition timing. By combining agronomically informed temporal inputs with a flexible spatial redistribution framework, DBYM enables the extension of yield map time series across seasons and provides a scalable and resource-efficient alternative for precision agriculture applications in fields lacking dedicated yield monitoring systems.","摘要 目的 产量图对农业决策至关重要，但现有方法往往针对特定作物或机器，限制了其更广泛的适用性。方法 本研究引入了一个基于农学知识的时间可迁移产量预测框架，该框架将最佳物候时刻（OPM）选择与空间产量再分配策略——基于密度的产量制图（DBYM）相结合。该框架依赖于在具有生理意义的作物阶段获取的卫星影像所衍生的植被指数，以及独立已知的田块尺度产量值来指导空间产量分配。该方法在巴西和澳大利亚的商业田块中进行了评估，涵盖10个生长季，涉及咖啡、甘蔗和小麦在人工与机械化生产系统下的种植。除DBYM外，还评估了使用相同OPM衍生输入的简单线性回归基线，以探究基于农学知识的时间输入选择本身的贡献。结果 结果表明，仅基于OPM的输入已在各生长季提供了较强的预测能力，而DBYM改善了预测的空间一致性，尤其是在校准样本稀疏的情景下。一致性指数范围为0.73至0.94，同时所有DBYM预测的Moran双变量指数均显著（p \u003C 0.01），证实了观测产量模式与预测产量模式之间的空间一致性。RMSE值与机器学习和深度学习产量预测研究中报道的值相当，巴西咖啡为0.21至0.28 Mg ha⁻¹，甘蔗为5.43至6.22 Mg ha⁻¹，小麦为0.15至0.26 Mg ha⁻¹，澳大利亚小麦为0.44至0.61 Mg ha⁻¹。结论 结果表明，预测性能可能受到获取时机农学相关性的影响。通过将基于农学知识的时间输入与灵活的空间再分配框架相结合，DBYM能够将产量图时间序列扩展至多个生长季，并为缺乏专用产量监测系统的田块提供了一种可扩展且资源高效的精准农业应用替代方案。",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出农学信息驱动的跨季可迁移产量预测框架，多作物多国验证，方法新颖且实用，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","产量预测","精准农业","遥感估产","咖啡",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,54,60,68,75,81,88],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":53},9,true,[],[49,50,51,52],"深度学习","机器学习","machine learning","deep learning",[],{"code":55,"score":20,"supported":46,"title":56,"abstract":57,"tags":59},"I1",[],[28,58],"precision agriculture",[26,28],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":67},"A2",6,[],[65,66],"小麦","wheat",[],{"code":69,"score":62,"supported":46,"title":70,"abstract":71,"tags":74},"A6",[],[72,73],"甘蔗","sugarcane",[],{"code":76,"score":62,"supported":46,"title":77,"abstract":78,"tags":80},"B3",[],[30,79],"coffee",[],{"code":82,"score":83,"supported":46,"title":84,"abstract":85,"tags":87},"F5",3,[],[86],"机械化",[],{"code":89,"score":90,"supported":14,"title":91,"abstract":92,"tags":93},"I3",2,[],[],[27,29],[95,96],"遥感 产量预测 咖啡 甘蔗 小麦","DBYM 产量制图 精准农业","遥感产量预测咖啡甘蔗小麦-3522","10.1007\u002Fs11119-026-10454-2",{"doi":98,"openalex_id":100,"authors":101,"venue":10,"cited_by_count":119,"oa_url":6,"card":120,"direction":126,"ingested_from":127},"W7214356320",[102,105,108,111,113,116],{"name":103,"orcid":104},"Ricardo Canal Filho","https:\u002F\u002Forcid.org\u002F0000-0001-8043-8325",{"name":106,"orcid":107},"José Paulo Molin","https:\u002F\u002Forcid.org\u002F0000-0001-7250-3780",{"name":109,"orcid":110},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":112,"orcid":9},"Eudocio Rafael Otavio da Silva",{"name":114,"orcid":115},"Luiz Gustavo de Góes Sterle","https:\u002F\u002Forcid.org\u002F0000-0001-9748-3923",{"name":117,"orcid":118},"Marcelo Chan Fu Wei","https:\u002F\u002Forcid.org\u002F0000-0002-8242-8435",0,{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"提出农学知情框架，结合最优物候期与密度产量制图，实现跨季节可迁移的产量预测。","利用卫星植被指数与最优物候期选择，结合密度产量制图DBYM，在巴西和澳大利亚多作","仅用最优物候期输入即可跨季节强预测，DBYM提升空间一致性，精度与机器学习方法相当。","农业遥感与作物表型","可探索将农学知情时间选择与深度时空模型结合，提升无产量监测系统田块的跨区域迁移能力。","农业人工智能与决策模型","openalex","2026-09-26T23:30:02.989455Z",{"total":62,"page":21,"page_size":62,"items":130},[131,211,325,381,433,483],{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":138,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":146,"tags":148,"primary_topic":151,"secondary_topics":157,"classification":159,"search_phrases":193,"slug":196,"view_count":119,"doi":197,"paper":198,"created_at":210},4801,"CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS","https:\u002F\u002Fdoi.org\u002F10.30546\u002F09090.2025.210.005","The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management.This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset.We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting.The results demonstrate the exceptional capability of tree-based models in this domain.Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions.For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84)and about 70% of the variance in yield potential (R²≈0.696).These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts.This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.","对可持续农业集约化的需求日益增长，亟需先进工具来优化作物选择与资源管理。本研究提出一个集成的分析框架，利用机器学习从综合性的CropIntel数据集中提取农业环境智能信息。我们针对三个关键精准农业任务评估了超过十五种机器学习算法：作物分类、季节性灌溉需求预测和产量潜力预报。结果表明，基于树的模型在该领域具有卓越能力。值得注意的是，决策树、装袋法和高斯朴素贝叶斯分类器在基于气候和土壤条件识别适宜作物方面达到了近乎完美的准确率（Acc≈99.7%）。在回归任务中，LightGBM和直方图梯度提升模型最为有效，分别解释了约84%的灌溉需求方差（R²≈0.84）和约70%的产量潜力方差（R²≈0.696）。这些发现凸显了机器学习在构建强大决策支持系统方面的潜力，可指导作物选择、优化水资源分配并提供可靠的产量预报。本研究为一种整体性、数据驱动的方法论做出了贡献，有助于提升农业实践的效率与可持续性。","JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE","2026-10-12T00:00:00Z",72,{"impact":141,"substance":142,"depth":143,"authority":144,"freshness":45,"relevant":21,"comment":145},12,21,17,13,"构建CropIntel农业环境数据集并系统评测15种以上机器学习算法，在作物分类、灌溉需求与产量预测上给出可复现结论，方法扎实但属细分领域学术进展，产业影响有限。",[147],{"name":137,"url":134},[26,149,27,28,150],"农业人工智能","灌溉决策",{"code":152,"slug":153,"name":154,"path":155,"parent_code":156},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[158],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":160},{"candidates":161,"agricultural_evidence":46},[162,168,173,177,183,189],{"code":152,"score":17,"supported":46,"title":163,"abstract":164,"tags":167},[153],[165,166,153],"灌溉","水资源",[150],{"code":32,"score":13,"supported":46,"title":169,"abstract":170,"tags":172},[],[50,51,171],"decision support",[149],{"code":55,"score":20,"supported":46,"title":174,"abstract":175,"tags":176},[],[28,58],[26,28],{"code":178,"score":83,"supported":46,"title":179,"abstract":180,"tags":182},"H2",[],[181],"土壤",[],{"code":184,"score":83,"supported":46,"title":185,"abstract":186,"tags":188},"H8",[],[187],"可持续农业",[],{"code":89,"score":21,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[27],[194,195],"CropIntel 数据集 作物分类","机器学习 灌溉需求 预测","CropIntel数据集作物分类-4801","10.30546\u002F09090.2025.210.005",{"doi":197,"openalex_id":199,"authors":200,"venue":137,"cited_by_count":119,"oa_url":134,"card":204,"direction":209,"ingested_from":127},"W7221521521",[201],{"name":202,"orcid":203},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":205,"method":206,"finding":207,"direction":126,"opportunity":208},"基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。","CropIntel农业环境数据集，对比15+种机器学习算法，含树模型与贝叶斯分类","树模型分类准确率近99.7%，LightGBM等回归模型对灌溉需求R²约0.84、产量约0.70。","可探索多任务联合建模与模型可解释性，并将预测结果嵌入实时灌溉决策系统。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:08.688669Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":223,"tags":225,"primary_topic":227,"secondary_topics":228,"classification":234,"search_phrases":309,"slug":312,"view_count":90,"doi":313,"paper":314,"created_at":324},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":17,"substance":221,"depth":143,"authority":144,"freshness":20,"relevant":21,"comment":222},20,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[224],{"name":217,"url":214},[26,149,27,226,28],"可解释AI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[229,230],{"code":152,"slug":153,"name":154,"path":155,"parent_code":156},{"code":184,"slug":231,"name":232,"path":233,"parent_code":156},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":39,"locked":14,"version":40,"method":41,"evidence":235},{"candidates":236,"agricultural_evidence":46},[237,246,250,255,262,269,274,281,287,293,299,305],{"code":32,"score":238,"supported":46,"title":239,"abstract":240,"tags":245},11,[],[241,50,242,51,243,244],"目标检测","计算机视觉","artificial intelligence","computer vision",[149,226],{"code":152,"score":45,"supported":46,"title":247,"abstract":248,"tags":249},[],[165,166,153],[],{"code":184,"score":20,"supported":46,"title":251,"abstract":253,"tags":254},[252],"sustainable agriculture",[],[],{"code":256,"score":62,"supported":46,"title":257,"abstract":258,"tags":261},"A8",[],[259,260],"粮食安全","food security",[],{"code":263,"score":62,"supported":46,"title":264,"abstract":265,"tags":268},"F3",[],[266,267],"肥料","fertilizer",[],{"code":178,"score":62,"supported":46,"title":270,"abstract":271,"tags":273},[],[181,272],"soil",[],{"code":275,"score":62,"supported":46,"title":276,"abstract":277,"tags":280},"I5",[],[278,279],"物联网","internet of things",[],{"code":89,"score":282,"supported":46,"title":283,"abstract":284,"tags":286},4,[],[285],"遥感",[27],{"code":288,"score":83,"supported":46,"title":289,"abstract":290,"tags":292},"A7",[],[291],"间作",[],{"code":294,"score":83,"supported":46,"title":295,"abstract":296,"tags":298},"F4",[],[297],"除草",[],{"code":300,"score":83,"supported":46,"title":301,"abstract":302,"tags":304},"H7",[],[303],"气候变化",[],{"code":55,"score":90,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[26,28],[310,311],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":313,"openalex_id":315,"authors":316,"venue":217,"cited_by_count":119,"oa_url":214,"card":319,"direction":209,"ingested_from":127},"W7212616413",[317],{"name":318,"orcid":9},"Manish Maan",{"tldr":320,"method":321,"finding":322,"direction":126,"opportunity":323},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":333,"is_selected":14,"score":334,"score_detail":335,"sources":339,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":366,"slug":368,"view_count":90,"doi":369,"paper":370,"created_at":380},2337,"High-Precision Crop Yield Prediction Model Combining GANs and Random Forest","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723504","Abstract Crop yield prediction algorithms are now much more accurate and useful thanks to recent developments in deep learning. In order to analyse crop yield, this study explores the combination of Random Forest methods with Generative Adversarial Networks (GANs). In order to overcome data scarcity and class imbalance, GANs are used for data augmentation, producing realistic synthetic samples that strengthen deep learning models. Various agro-climatic and soil datasets are used to forecast yield using Random Forest, an ensemble machine learning technique. According to comparative analyses, Random Forest outperforms conventional regression models and has great generalization across areas and crops4568, regularly achieving high predictive accuracy (R2 > 0.95). A potent foundation for precision agriculture is provided by the complementary application of Random Forest for prediction and GANs for data enrichment, allowing better precision in crop yield analysis. With proper hyperparameter tuning, RF can achieve very high accuracy (R² up to 0.99 in some studies), making it a preferred choice for practical yield forecasting.","摘要 得益于深度学习的最新发展，作物产量预测算法如今已更加准确和实用。为了分析作物产量，本研究探索了随机森林方法与生成对抗网络（Generative Adversarial Networks，GANs）的结合。为了克服数据稀缺和类别不平衡问题，研究使用GANs进行数据增强，生成逼真的合成样本以强化深度学习模型。研究采用多种农业气候和土壤数据集，利用随机森林这一集成机器学习技术来预测产量。比较分析表明，随机森林优于传统回归模型，并在不同地区和作物间展现出强大的泛化能力，通常能够实现较高的预测精度（R2 > 0.95）。随机森林用于预测与GANs用于数据增强的互补应用，为精准农业提供了有力的基础，使作物产量分析能够实现更高的精度。通过适当的超参数调优，随机森林可以达到非常高的精度（在某些研究中R²高达0.99），使其成为实际产量预测的首选方法。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,61,{"impact":336,"substance":17,"depth":337,"authority":141,"freshness":119,"relevant":21,"comment":338},16,15,"将GAN数据增强与随机森林结合用于作物产量预测，方法组合有新意且精度结论明确，但属预印本平台论文、发布日期异常且时效性差，暂不宜进入每日精选。",[340,341],{"name":331,"url":328},{"name":331,"url":342},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723505",[26,149,27,28,344],"数据增强",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,354,358,362],{"code":32,"score":13,"supported":46,"title":351,"abstract":352,"tags":353},[],[49,50,51,52],[149],{"code":55,"score":20,"supported":46,"title":355,"abstract":356,"tags":357},[],[28,58],[26,28],{"code":178,"score":62,"supported":46,"title":359,"abstract":360,"tags":361},[],[181,272],[],{"code":89,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[27],[367,311],"农业人工智能 产量预测 数据增强 智慧农业","农业人工智能产量预测数据增强智慧农业-2337","10.5281\u002Fzenodo.22723504",{"doi":369,"openalex_id":371,"authors":372,"venue":331,"cited_by_count":119,"oa_url":328,"card":375,"direction":126,"ingested_from":127},"W7212353714",[373],{"name":374,"orcid":9},"S. Kavitha",{"tldr":376,"method":377,"finding":378,"direction":126,"opportunity":379},"结合GAN数据增强与随机森林，构建高精度作物产量预测模型。","GAN生成合成样本缓解数据稀缺，随机森林基于农业气候与土壤数据预测。","随机森林预测精度高（R²>0.95，部分达0.99），优于传统回归模型。","可探索GAN生成样本的农学合理性验证及跨区域迁移学习以提升泛化能力。","2026-09-13T23:30:43.223973Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":9,"source_name":331,"source_url":384,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":391,"tags":393,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":413,"slug":416,"view_count":119,"doi":417,"paper":418,"created_at":432},5587,"Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544195","Computer vision yield models deployed in agricultural fields are routinely paired with retrofit sensor packages that replace or supplement the original imaging hardware, and the resulting calibration shift can silently invalidate the model's predictive guarantees. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80% power at a 5 percentage point effect with 54 clusters. The paper states a formal guarantee: under stated assumptions on the stability of the calibration map, the before-after-control-impact estimator yields a consistent and asymptotically normal estimate of the calibration shift, and the associated confidence interval covers the true shift at the nominal rate. The contribution is a design-theoretic account of how retrofit-induced calibration shift can be detected and bounded without observational data, together with the sample-size requirements that follow.","部署在农田中的计算机视觉产量模型通常与改装传感器组件配合使用，这些组件用于替换或补充原始成像硬件，由此产生的校准偏移可能悄然使模型的预测保证失效。该分析在声明的方差结构下刻画了设计的工作特性，其中组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64，每个设计单元采用2000次重复，聚类数分别为8、16、24和32，效应量为3、5和8个百分点。该设计在54个聚类、5个百分点效应下达到80%的检验力。论文给出了一项形式化保证：在关于校准映射稳定性的既定假设下，前后对照影响（before-after-control-impact）估计量给出校准偏移的一致且渐近正态的估计，且相关置信区间以名义覆盖率覆盖真实偏移。其贡献在于从设计理论角度说明了如何在没有观测数据的情况下检测并界定改装引起的校准偏移，以及随之而来的样本量要求。","2026-10-21T00:00:00Z",65,{"impact":20,"substance":17,"depth":143,"authority":141,"freshness":13,"relevant":21,"comment":390},"方法学扎实、统计设计严谨的预印本论文，但偏理论设计层面，缺乏田间实证数据，产业影响有限，适合作为技术参考而非每日精选头条。",[392],{"name":331,"url":384},[26,149,242,27,394],"传感器校准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,405,409],{"code":32,"score":401,"supported":46,"title":402,"abstract":403,"tags":404},24,[243,244],[242,244],[149,242],{"code":55,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[26],{"code":89,"score":21,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[27],[414,415],"计算机视觉 产量预测 传感器校准","农业人工智能 传感器校准 计算机视觉 产量预测","计算机视觉产量预测传感器校准-5587","10.5281\u002Fzenodo.23544195",{"doi":417,"openalex_id":419,"authors":420,"venue":331,"cited_by_count":119,"oa_url":384,"card":427,"direction":126,"ingested_from":127},"W7223104380",[421,423,425],{"name":422,"orcid":9},"Tigist Alemu Gebremariam",{"name":424,"orcid":9},"Hawi Tesfaye Desta",{"name":426,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":428,"method":429,"finding":430,"direction":126,"opportunity":431},"提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。","用前后对照影响估计量，基于方差结构做2000次重复的模拟设计分析。","5个百分点效应下54个集群可达80%检验力，估计量一致且渐近正态。","可将该设计理论扩展到多传感器异构替换与在线校准监测，填补真实田间验证空白。","2026-10-22T11:21:01.554995Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":331,"source_url":436,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":446,"secondary_topics":447,"classification":452,"search_phrases":468,"slug":470,"view_count":119,"doi":471,"paper":472,"created_at":482},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":20,"substance":221,"depth":143,"authority":141,"freshness":13,"relevant":21,"comment":441},"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。",[443],{"name":331,"url":436},[26,149,27,285,445],"可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[448],{"code":89,"slug":449,"name":450,"path":451,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":453},{"candidates":454,"agricultural_evidence":46},[455,459,464],{"code":32,"score":141,"supported":46,"title":456,"abstract":457,"tags":458},[244],[242],[149],{"code":89,"score":20,"supported":46,"title":460,"abstract":461,"tags":463},[],[285,462],"remote sensing",[27,285],{"code":55,"score":21,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[26],[469,311],"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可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":471,"openalex_id":473,"authors":474,"venue":331,"cited_by_count":119,"oa_url":436,"card":477,"direction":124,"ingested_from":127},"W7223114002",[475],{"name":476,"orcid":9},"Kadiatou Barry",{"tldr":478,"method":479,"finding":480,"direction":124,"opportunity":481},"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":484,"title":485,"url":486,"summary":487,"summary_zh":488,"content":9,"source_name":489,"source_url":486,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":490,"sources":492,"tags":494,"primary_topic":496,"secondary_topics":497,"classification":503,"search_phrases":544,"slug":547,"view_count":119,"doi":548,"paper":549,"created_at":567},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",{"impact":17,"substance":18,"depth":17,"authority":144,"freshness":45,"relevant":21,"comment":491},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[493],{"name":489,"url":486},[26,149,278,28,495],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[498,502],{"code":275,"slug":499,"name":500,"path":501,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":152,"slug":153,"name":154,"path":155,"parent_code":156},{"status":39,"locked":14,"version":40,"method":41,"evidence":504},{"candidates":505,"agricultural_evidence":46},[506,510,514,521,525,531,535,539],{"code":32,"score":17,"supported":46,"title":507,"abstract":508,"tags":509},[243],[49,50,242,51,52,244],[149],{"code":55,"score":336,"supported":46,"title":511,"abstract":512,"tags":513},[58],[28,58],[26,28],{"code":275,"score":13,"supported":46,"title":515,"abstract":516,"tags":520},[],[278,517,518,519],"边缘计算","IoT","edge computing",[278],{"code":152,"score":45,"supported":46,"title":522,"abstract":523,"tags":524},[],[165,166,153],[],{"code":300,"score":526,"supported":46,"title":527,"abstract":528,"tags":530},7,[],[303,529],"climate change",[495],{"code":178,"score":62,"supported":46,"title":532,"abstract":533,"tags":534},[],[181,272],[],{"code":288,"score":83,"supported":46,"title":536,"abstract":537,"tags":538},[],[291],[],{"code":263,"score":83,"supported":46,"title":540,"abstract":541,"tags":543},[],[542],"养分管理",[],[545,546],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":548,"openalex_id":550,"authors":551,"venue":489,"cited_by_count":119,"oa_url":486,"card":562,"direction":209,"ingested_from":127},"W7223121456",[552,555,557,559],{"name":553,"orcid":554},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":556,"orcid":9},"Oluwadare OYEBODE",{"name":558,"orcid":9},"Iretiayo Adelaiye",{"name":560,"orcid":561},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":563,"method":564,"finding":565,"direction":126,"opportunity":566},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z"]