[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2518":3,"related-2518":161},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":50,"search_phrases":141,"slug":144,"view_count":137,"doi":145,"paper":146,"created_at":160},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。",null,"Indian Journal of Agricultural Research","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","产量预测","可解释AI","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,68,75,80,88,95,102,109,116,123,129,135],{"code":33,"score":57,"supported":58,"title":59,"abstract":60,"tags":67},11,true,[],[61,62,63,64,65,66],"目标检测","机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[28,30],{"code":40,"score":69,"supported":58,"title":70,"abstract":71,"tags":74},9,[],[72,73,41],"灌溉","水资源",[],{"code":46,"score":21,"supported":58,"title":76,"abstract":78,"tags":79},[77],"sustainable agriculture",[],[],{"code":81,"score":82,"supported":58,"title":83,"abstract":84,"tags":87},"A8",6,[],[85,86],"粮食安全","food security",[],{"code":89,"score":82,"supported":58,"title":90,"abstract":91,"tags":94},"F3",[],[92,93],"肥料","fertilizer",[],{"code":96,"score":82,"supported":58,"title":97,"abstract":98,"tags":101},"H2",[],[99,100],"土壤","soil",[],{"code":103,"score":82,"supported":58,"title":104,"abstract":105,"tags":108},"I5",[],[106,107],"物联网","internet of things",[],{"code":110,"score":111,"supported":58,"title":112,"abstract":113,"tags":115},"I3",4,[],[114],"遥感",[29],{"code":117,"score":118,"supported":58,"title":119,"abstract":120,"tags":122},"A7",3,[],[121],"间作",[],{"code":124,"score":118,"supported":58,"title":125,"abstract":126,"tags":128},"F4",[],[127],"除草",[],{"code":130,"score":118,"supported":58,"title":131,"abstract":132,"tags":134},"H7",[],[133],"气候变化",[],{"code":136,"score":137,"supported":14,"title":138,"abstract":139,"tags":140},"I1",2,[],[],[27,31],[142,143],"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":145,"openalex_id":147,"authors":148,"venue":10,"cited_by_count":151,"oa_url":6,"card":152,"direction":158,"ingested_from":159},"W7212616413",[149],{"name":150,"orcid":9},"Manish Maan",0,{"tldr":153,"method":154,"finding":155,"direction":156,"opportunity":157},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-15T23:30:13.705421Z",{"total":82,"page":22,"page_size":82,"items":162},[163,236,302,393,469,533],{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":169,"source_url":166,"published_at":170,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":171,"score_detail":172,"sources":174,"tags":176,"primary_topic":180,"secondary_topics":181,"classification":184,"search_phrases":210,"slug":213,"view_count":21,"doi":214,"paper":215,"created_at":235},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",77,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":173},"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[175],{"name":169,"url":166},[27,28,177,178,179],"智能灌溉","节水农业","成本效益分析",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[182,183],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":185},{"candidates":186,"agricultural_evidence":58},[187,192,198,202,206],{"code":40,"score":188,"supported":58,"title":189,"abstract":190,"tags":191},19,[41],[72,73,41],[177,178],{"code":46,"score":193,"supported":58,"title":194,"abstract":195,"tags":197},14,[77],[196,77],"可持续农业",[],{"code":33,"score":13,"supported":58,"title":199,"abstract":200,"tags":201},[],[62,64,65],[28],{"code":96,"score":82,"supported":58,"title":203,"abstract":204,"tags":205},[],[99,100],[],{"code":136,"score":22,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[27],[211,212],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":214,"openalex_id":216,"authors":217,"venue":169,"cited_by_count":151,"oa_url":166,"card":230,"direction":158,"ingested_from":159},"W7214115789",[218,220,222,224,226,228],{"name":219,"orcid":9},"Venkata Suman Jami",{"name":221,"orcid":9},"Purushotham Prasad Kalisetti",{"name":223,"orcid":9},"Sampath Dakshina Murthy A.",{"name":225,"orcid":9},"Gurunadha R.",{"name":227,"orcid":9},"Hema Mamidipaka",{"name":229,"orcid":9},"Gurrapu Omprakash",{"tldr":231,"method":232,"finding":233,"direction":158,"opportunity":234},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":9,"source_name":242,"source_url":239,"published_at":243,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":249,"tags":251,"primary_topic":253,"secondary_topics":254,"classification":256,"search_phrases":285,"slug":288,"view_count":151,"doi":289,"paper":290,"created_at":301},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":246,"substance":247,"depth":19,"authority":20,"freshness":69,"relevant":22,"comment":248},12,21,"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。",[250],{"name":242,"url":239},[27,28,29,31,252],"灌溉决策",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[255],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":257},{"candidates":258,"agricultural_evidence":58},[259,263,268,273,277,281],{"code":40,"score":17,"supported":58,"title":260,"abstract":261,"tags":262},[41],[72,73,41],[252],{"code":33,"score":13,"supported":58,"title":264,"abstract":265,"tags":267},[],[62,64,266],"decision support",[28],{"code":136,"score":21,"supported":58,"title":269,"abstract":270,"tags":272},[],[31,271],"precision agriculture",[27,31],{"code":96,"score":118,"supported":58,"title":274,"abstract":275,"tags":276},[],[99],[],{"code":46,"score":118,"supported":58,"title":278,"abstract":279,"tags":280},[],[196],[],{"code":110,"score":22,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[29],[286,287],"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":289,"openalex_id":291,"authors":292,"venue":242,"cited_by_count":151,"oa_url":239,"card":296,"direction":158,"ingested_from":159},"W7221521521",[293],{"name":294,"orcid":295},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":297,"method":298,"finding":299,"direction":156,"opportunity":300},"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。","2026-10-13T23:30:08.688669Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":9,"source_name":308,"source_url":305,"published_at":309,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":318,"secondary_topics":319,"classification":325,"search_phrases":369,"slug":372,"view_count":151,"doi":373,"paper":374,"created_at":392},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)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":312,"depth":17,"authority":20,"freshness":69,"relevant":22,"comment":313},22,"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。",[315],{"name":308,"url":305},[27,28,106,31,317],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[320,324],{"code":103,"slug":321,"name":322,"path":323,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":326},{"candidates":327,"agricultural_evidence":58},[328,334,339,346,350,356,360,364],{"code":33,"score":17,"supported":58,"title":329,"abstract":330,"tags":333},[65],[331,62,63,64,332,66],"深度学习","deep learning",[28],{"code":136,"score":335,"supported":58,"title":336,"abstract":337,"tags":338},16,[271],[31,271],[27,31],{"code":103,"score":13,"supported":58,"title":340,"abstract":341,"tags":345},[],[106,342,343,344],"边缘计算","IoT","edge computing",[106],{"code":40,"score":69,"supported":58,"title":347,"abstract":348,"tags":349},[],[72,73,41],[],{"code":130,"score":351,"supported":58,"title":352,"abstract":353,"tags":355},7,[],[133,354],"climate change",[317],{"code":96,"score":82,"supported":58,"title":357,"abstract":358,"tags":359},[],[99,100],[],{"code":117,"score":118,"supported":58,"title":361,"abstract":362,"tags":363},[],[121],[],{"code":89,"score":118,"supported":58,"title":365,"abstract":366,"tags":368},[],[367],"养分管理",[],[370,371],"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":373,"openalex_id":375,"authors":376,"venue":308,"cited_by_count":151,"oa_url":305,"card":387,"direction":158,"ingested_from":159},"W7223121456",[377,380,382,384],{"name":378,"orcid":379},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":381,"orcid":9},"Oluwadare OYEBODE",{"name":383,"orcid":9},"Iretiayo Adelaiye",{"name":385,"orcid":386},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":388,"method":389,"finding":390,"direction":156,"opportunity":391},"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",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":399,"source_url":396,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":400,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":444,"slug":446,"view_count":137,"doi":447,"paper":448,"created_at":468},2537,"A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fai7090364","Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t\u002Fha and R2=0.92, outperforming the evaluated baselines with statistically significant differences (p\u003C0.05). The fully adjusted fixed-effects model identifies a positive association between higher sustainability states and yield, while retrospective Digital Twin replay demonstrates close temporal agreement between predicted and observed outcomes. Model-based scenario simulations indicate predicted yield increases of up to 12.4% under the evaluated sustainability-state perturbations; these estimates represent counterfactual sensitivity rather than guaranteed causal effects. TSAFI-DT provides a reproducible framework for sustainability-aware agricultural forecasting, comparative scenario exploration, and data-driven decision support.","可持续农业规划需要能够捕捉时空变异性、可持续性动态以及替代管理情景潜在结果的预测框架。本研究提出TSAFI-DT，一个经回溯验证的数据驱动数字孪生（Digital Twin）原型，集成了时空数据重建、可持续性状态表征、分层产量预测、反事实分析和情景模拟。该框架基于历史地区级APY观测数据运行，因此代表的是数字孪生运行的回溯近似，而非持续同步的网络-物理农业数字孪生。扩展再生农业指数（Extended Regenerative Agriculture Index, eRAI）综合了作物多样性、生产力-稳定性、土地利用效率和产量趋势信息，以刻画地区级可持续性状态。采用贝叶斯优化的XGBoost模型在时间验证下进行一步超前产量预测，同时固定效应和合成控制分析提供互补的关联性证据和干预关联性证据。利用印度1997—2023年地区级花生数据进行评估，结果表明所提出的预测器实现了0.171 t\u002Fha的RMSE和R²=0.92，优于所评估的基线模型且差异具有统计学显著性（p\u003C0.05）。完全调整的固定效应模型识别出较高可持续性状态与产量之间的正相关关系，而回溯性数字孪生重放表明预测结果与观测结果在时间上高度一致。基于模型的情景模拟显示，在所评估的可持续性状态扰动下，预测产量增幅最高可达12.4%；这些估计代表的是反事实敏感性而非保证的因果效应。TSAFI-DT为可持续性感知的农业预测、比较情景探索和数据驱动决策支持提供了一个可复现的框架。","AI",{"impact":335,"substance":312,"depth":17,"authority":246,"freshness":21,"relevant":22,"comment":401},"提出融合数字孪生、反事实分析与贝叶斯优化XGBoost的花生产量预测框架，方法新颖、数据跨度长且验证充分，对可持续农业决策支持有参考价值。",[403],{"name":399,"url":396},[27,28,29,405,406],"数字孪生","花生种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[409],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":411},{"candidates":412,"agricultural_evidence":58},[413,417,423,428,434,440],{"code":33,"score":246,"supported":58,"title":414,"abstract":415,"tags":416},[266],[266],[28],{"code":46,"score":69,"supported":58,"title":418,"abstract":419,"tags":422},[],[196,420,421],"再生农业","regenerative agriculture",[],{"code":136,"score":21,"supported":58,"title":424,"abstract":425,"tags":427},[],[405,426],"digital twin",[405,27],{"code":429,"score":111,"supported":58,"title":430,"abstract":431,"tags":433},"A4",[],[432],"花生",[406],{"code":435,"score":118,"supported":58,"title":436,"abstract":437,"tags":439},"J1",[],[438],"农业规划",[],{"code":110,"score":22,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[29],[445,143],"农业人工智能 产量预测 数字孪生 智慧农业","农业人工智能产量预测数字孪生智慧农业-2537","10.3390\u002Fai7090364",{"doi":447,"openalex_id":449,"authors":450,"venue":399,"cited_by_count":151,"oa_url":396,"card":462,"direction":467,"ingested_from":159},"W7212812029",[451,454,457,459],{"name":452,"orcid":453},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":455,"orcid":456},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":458,"orcid":9},"Sumendra Yogarayan",{"name":460,"orcid":461},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",{"tldr":463,"method":464,"finding":465,"direction":156,"opportunity":466},"提出TSAFI-DT数字孪生框架，用贝叶斯优化XGBoost预测印度花生产量并做反事实情景分析。","基于1997-2023年印度县级花生产量数据，构建eRAI可持续性指数，采用贝叶","模型RMSE为0.171 t\u002Fha、R²=0.92，高可持续状态与产量正相关，情景模拟产量最高提升1","可延伸至实时物联网数据驱动的数字孪生，并验证反事实情景的因果效应与跨作物泛化能力。","数字乡村与农业信息化","2026-09-15T23:30:26.594281Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":9,"content":9,"source_name":474,"source_url":472,"published_at":475,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":483,"secondary_topics":484,"classification":486,"search_phrases":515,"slug":518,"view_count":137,"doi":519,"paper":520,"created_at":532},1267,"Artificial Intelligence for Climate-Smart Agriculture: A Review of Applications in Yield Prediction, Weather Forecasting, Irrigation Management and Pest and Disease Detection","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug707","Climate change is increasingly disrupting agricultural systems worldwide through more frequent extreme weather events, shifting rainfall patterns, and rising temperatures. These changes contribute to reduced crop yields, unstable food supplies, and heightened production risks, especially in vulnerable developing regions. In response, ClimateSmart Agriculture (CSA) has been developed as a strategic framework to improve agricultural productivity while enhancing resilience and promoting environmentally sustainable farming practices. Within this framework, Artificial Intelligence (AI) is increasingly recognized as a transformative tool capable of supporting data-driven agricultural decision-making. This review systematically synthesizes recent literature on AI applications in CSA, focusing on four key thematic areas: crop yield prediction, weather and climate forecasting, irrigation and water management, and pest and disease detection under climate stress. Peer-reviewed articles published between 2020 and 2026 were retrieved from Google Scholar, Semantic Scholar, and Crossref. A total of 170 studies were initially identified, of which 58 highly relevant articles were selected following screening based on relevance, quality, and thematic alignment. Findings indicate that AIbased systems significantly improve predictive accuracy, optimize resource use, and enhance early warning capabilities across agricultural systems. Machine learning, deep learning, remote sensing, IoT, and big data analytics are widely applied to support precision agriculture and climate adaptation strategies. However, key challenges such as data scarcity in developing regions, high implementation costs, limited farmer adoption, and climate uncertainty continue to constrain widespread adoption. The review concludes that AI holds strong potential to transform CSA by improving productivity, resilience, and sustainability. Nonetheless, its effectiveness depends on the development of accessible, affordable, and context-specific solutions supported by strong policy frameworks and improved digital infrastructure.","International Journal of Innovative Science and Research Technology (IJISRT)","2026-08-29T00:00:00Z",67,{"impact":17,"substance":18,"depth":17,"authority":21,"freshness":118,"relevant":22,"comment":478},"系统综述AI在气候智慧农业中的应用，覆盖产量预测、灌溉、病虫害等，信息量大，但来源权威性一般且时效性较低。",[480],{"name":474,"url":472},[27,28,482,31,133],"综述",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[485],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":487},{"candidates":488,"agricultural_evidence":58},[489,493,498,502,506,511],{"code":33,"score":17,"supported":58,"title":490,"abstract":491,"tags":492},[65],[64,332,65],[28],{"code":136,"score":20,"supported":58,"title":494,"abstract":496,"tags":497},[495],"smart agriculture",[271],[27,31],{"code":40,"score":57,"supported":58,"title":499,"abstract":500,"tags":501},[41],[41],[],{"code":130,"score":111,"supported":58,"title":503,"abstract":504,"tags":505},[],[354],[133],{"code":110,"score":118,"supported":58,"title":507,"abstract":508,"tags":510},[],[509],"remote sensing",[],{"code":103,"score":118,"supported":58,"title":512,"abstract":513,"tags":514},[],[343],[],[516,517],"农业人工智能 智慧农业 气候变化 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业气候变化精准农业-1267","10.38124\u002Fijisrt\u002F26aug707",{"doi":519,"openalex_id":521,"authors":522,"venue":474,"cited_by_count":151,"oa_url":526,"card":527,"direction":158,"ingested_from":159},"W7204643386",[523],{"name":524,"orcid":525},"Sonny Gad Attipoe","https:\u002F\u002Forcid.org\u002F0000-0002-8981-6756","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG707.pdf",{"tldr":528,"method":529,"finding":530,"direction":156,"opportunity":531},"综述AI在气候智慧农业中的应用，涵盖产量预测、天气预报、灌溉管理和病虫害检测。","系统综述2020-2026年文献，筛选58篇相关研究，分析ML、DL、遥感、Io","AI显著提升预测精度和资源利用，但面临数据稀缺、成本高、采纳率低等挑战。","可研究低成本、易用的AI解决方案，结合政策与数字基础设施，促进发展中国家CSA采纳。","2026-09-01T04:03:12.892133Z",{"id":534,"title":535,"url":536,"summary":537,"summary_zh":538,"content":9,"source_name":539,"source_url":536,"published_at":309,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":540,"score_detail":541,"sources":543,"tags":545,"primary_topic":548,"secondary_topics":549,"classification":551,"search_phrases":571,"slug":574,"view_count":151,"doi":575,"paper":576,"created_at":588},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)",69,{"impact":21,"substance":312,"depth":17,"authority":246,"freshness":69,"relevant":22,"comment":542},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[544],{"name":539,"url":536},[27,28,252,546,547],"安全保证","试验设计",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[550],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":552},{"candidates":553,"agricultural_evidence":58},[554,559,563,567],{"code":40,"score":555,"supported":58,"title":556,"abstract":557,"tags":558},15,[41],[72,41],[252],{"code":33,"score":246,"supported":58,"title":560,"abstract":561,"tags":562},[65],[266],[28],{"code":96,"score":82,"supported":58,"title":564,"abstract":565,"tags":566},[],[99,100],[],{"code":136,"score":22,"supported":14,"title":568,"abstract":569,"tags":570},[],[],[27],[572,573],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":575,"openalex_id":577,"authors":578,"venue":539,"cited_by_count":151,"oa_url":536,"card":583,"direction":156,"ingested_from":159},"W7223131205",[579,581],{"name":580,"orcid":9},"Nassima Benkhelifa",{"name":582,"orcid":9},"Yacine Aït Ahmed",{"tldr":584,"method":585,"finding":586,"direction":156,"opportunity":587},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z"]