[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2036":3,"related-2036":98},{"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":39,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":97},2036,"Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1910196","Crop recommendation is a vital part of precision agriculture as it helps farmers choose appropriate crops according to the nutrient profile and environmental conditions. This paper presents a crop recommendation framework in which Multi-Layer Perceptron (MLP), XGBoost, and Tab Transformer are first evaluated as baseline prediction models, followed by the proposed Krill Herd Optimization (KHO)-based explainable framework integrated with Explainable Artificial Intelligence (XAI). These eleven parameters are created based on agronomic and environmental aspects, namely: Nitrogen, Phosphorus, Potassium, Copper, Iron, Magnesium, Sulphur, Temperature, Rainfall, pH and Humidity. For better model transparency and to aid informed decision-making, model explanations with SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are used to identify feature contributions to crop predictions both locally and globally. The experimental results showed that Tab Transformer significantly outperformed the other models, with an accuracy of 0.99, precision of 0.98, recall of 0.99 and F1-score of 0.98. The proposed framework further incorporates Krill Herd Optimization (KHO) to generate optimized nutrient and climate profiles, while Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) are used for comparative evaluation of optimization performance. By combining explainable AI with optimization methods, the framework improves crop suitability prediction and provides transparent insights into the factors influencing crop recommendations, ensuring reliable decision support for practical farming applications. The proposed framework supports precision agriculture by enabling data-driven crop selection, reducing unnecessary fertilizer usage, optimizing crop productivity, and promoting sustainable farming practices.","作物推荐是精准农业的重要组成部分，有助于农民根据养分状况和环境条件选择适宜的作物。本文提出了一个作物推荐框架，首先评估多层感知机（MLP）、XGBoost和Tab Transformer作为基线预测模型，随后提出基于磷虾群优化（KHO）的可解释框架，并集成可解释人工智能（XAI）。基于农艺和环境因素构建了十一个参数，即：氮、磷、钾、铜、铁、镁、硫、温度、降雨量、pH值和湿度。为提高模型透明度并辅助知情决策，采用SHAP（SHapley加性解释）和LIME（局部可解释模型无关解释）进行模型解释，以在局部和全局层面识别特征对作物预测的贡献。实验结果表明，Tab Transformer显著优于其他模型，准确率为0.99，精确率为0.98，召回率为0.99，F1分数为0.98。所提框架进一步引入磷虾群优化（KHO）以生成优化的养分和气候方案，同时使用遗传算法（GA）、粒子群优化（PSO）和模拟退火（SA）对优化性能进行比较评估。通过将可解释人工智能与优化方法相结合，该框架提高了作物适宜性预测能力，并提供了影响作物推荐因素的透明洞察，确保为实际农业应用提供可靠的决策支持。所提框架通过实现数据驱动的作物选择、减少不必要的肥料使用、优化作物生产力并促进可持续农业实践，为精准农业提供支持。",null,"Frontiers in Artificial Intelligence","2026-09-09T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"融合营养与气候数据、Krill Herd优化与可解释AI的作物推荐框架，方法新颖、结论可靠，对精准农业决策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","精准施肥","可解释AI","作物推荐",{"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",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,68],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},8,true,[],[50,51],"artificial intelligence","decision support",[28,30],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"F3",7,[],[58,59],"肥料","fertilizer",[29],{"code":62,"score":55,"supported":47,"title":63,"abstract":64,"tags":67},"I1",[],[65,66],"精准农业","precision agriculture",[27],{"code":69,"score":70,"supported":47,"title":71,"abstract":72,"tags":74},"H8",3,[],[73],"可持续农业",[],[76,77],"农业人工智能 作物推荐 智慧农业 精准施肥","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业精准施肥-2036",2,"10.3389\u002Ffrai.2026.1910196",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":95,"ingested_from":96},"W7211994206",[84,86],{"name":85,"orcid":9},"P. Latha",{"name":87,"orcid":9},"P. Kumaresan",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"提出融合养分与气候数据、KHO优化和可解释AI的作物推荐框架，Tab Transformer精度达0","用MLP、XGBoost、Tab Transformer建模，结合KHO优化与S","Tab Transformer预测最优（准确率0.99），KHO优化养分气候方案，XAI提升推荐透明","农业人工智能与决策模型","可探索将可解释AI与优化算法嵌入真实农田物联网实时数据流，验证跨区域泛化与农户采纳效果。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.054372Z",{"total":99,"page":22,"page_size":99,"items":100},6,[101,177,241,319,395,461],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":114,"tags":116,"primary_topic":118,"secondary_topics":123,"classification":125,"search_phrases":158,"slug":161,"view_count":70,"doi":162,"paper":163,"created_at":176},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",62,{"impact":111,"substance":19,"depth":112,"authority":99,"freshness":21,"relevant":22,"comment":113},12,17,"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[115],{"name":107,"url":104},[27,28,29,30,117],"水肥一体化",{"code":54,"slug":119,"name":120,"path":121,"parent_code":122},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[124],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":126},{"candidates":127,"agricultural_evidence":47},[128,135,140,144,151],{"code":54,"score":129,"supported":47,"title":130,"abstract":132,"tags":134},19,[131],"fertigation",[133,58,59,131],"施肥",[117,29],{"code":33,"score":20,"supported":47,"title":136,"abstract":137,"tags":139},[50],[138],"机器学习",[28,30],{"code":62,"score":21,"supported":47,"title":141,"abstract":142,"tags":143},[66],[],[27],{"code":145,"score":99,"supported":47,"title":146,"abstract":147,"tags":150},"H3",[],[148,149],"灌溉","irrigation",[],{"code":152,"score":99,"supported":47,"title":153,"abstract":154,"tags":157},"I5",[],[155,156],"物联网","IoT",[],[159,160],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":162,"openalex_id":164,"authors":165,"venue":107,"cited_by_count":88,"oa_url":170,"card":171,"direction":95,"ingested_from":96},"W7212934364",[166,168],{"name":167,"orcid":9},"Shraddha S. Tayade",{"name":169,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":172,"method":173,"finding":174,"direction":93,"opportunity":175},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":191,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":220,"slug":222,"view_count":79,"doi":223,"paper":224,"created_at":240},2005,"KRISHI.AI: an explainable machine learning framework for data-driven crop recommendation","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-68815-w","Abstract Accurate crop selection is a fundamental determinant of farm productivity under varying soil characteristics and climatic conditions. Most current AI-based advisory systems are confined to predictive outputs or generic recommendations, providing little transparency into the reasoning behind a particular crop suggestion. This paper presents KRISHI.AI (derived from the Hindi word “krishi,”meaning“ agriculture,” and“ AI”for“ artificial intelligence), an agriculture-focused explainable artificial intelligence framework for data-driven crop recommendation. The framework recommends suitable crops from seven agronomic and environmental inputs, namely nitrogen (N), phosphorus (P), potassium (K), soil pH, temperature, humidity, and rainfall. It integrates a LightGBM-based multiclass prediction model with SHAP-based feature attribution and agronomically constrained counterfactual “what-if” analysis to explain why a crop is recommended and how feasible changes in the input conditions may alter the recommendation. The system uses a gradient-boosted tree ensemble (LightGBM) to produce high-confidence crop recommendations across 22 crop classes, achieving 99.39% test accuracy and a macro-averaged F1-score of 0.9900. These predictions are supplemented by SHAP (SHapley Additive exPlanations)-based feature-attribution visualizations and counterfactual “what-if” analysis to explain how input factors influence recommendations and how manipulating field conditions modifies crop suitability. Quantitative evaluation of the counterfactual module across 100 test instances exhibits a validity rate of 94.60%, a mean L1 feature-change distance of 0.18, and a mean of 2.10 features modified per scenario-confirming the practical credibility and sparsity of the generated explanations. These capabilities are delivered through an interactive Streamlit web dashboard that provides intuitive real-time feedback, enabling farmers and agricultural practitioners to explore scenario-based decisions without technical expertise. By integrating high-accuracy prediction with human-focused explainability in a single lightweight framework (4.3 MB model, 187 ms response latency), KRISHI.AI advances accessible and trustworthy decision support for technology-driven agriculture, with particular relevance to agricultural decision-support applications serving India’s smallholder farming community. The 189.3 ms value corresponds to the dedicated ablation profiling configuration, whereas 187 ms represents the representative end-to-end latency of the deployed pipeline reported in the main computational profiling experiment. The reported accuracy should be understood as a benchmark performance rather than as evidence of field-ready predictive reliability, because the uniformly balanced dataset does not capture the measurement noise, missing values, spatial heterogeneity, and seasonal variability present in real-world agricultural environments. However, the explainability pipeline and architectural contributions provide a reproducible basis for transparent agricultural AI.","准确的作物选择是决定不同土壤特性和气候条件下农业生产力的基本因素。当前大多数基于人工智能的咨询系统仅限于预测输出或通用建议，对特定作物建议背后的推理过程缺乏透明度。本文提出了KRISHI.AI（名称源自印地语词汇“krishi”，意为“农业”，以及“AI”，即“人工智能”），一个面向农业的可解释人工智能框架，用于数据驱动的作物推荐。该框架基于七项农艺与环境输入参数推荐适宜作物，即氮（N）、磷（P）、钾（K）、土壤pH值、温度、湿度及降雨量。它集成了基于LightGBM的多分类预测模型、基于SHAP的特征归因分析以及受农艺约束的反事实“假设”分析，以解释为何推荐某种作物，以及输入条件的可行变化如何改变推荐结果。该系统采用梯度提升树集成模型（LightGBM）在22个作物类别中生成高置信度的作物推荐，测试准确率达到99.39%，宏平均F1分数为0.9900。这些预测辅以基于SHAP（Shapley加性解释）的特征归因可视化和反事实“假设”分析，以解释输入因素如何影响推荐结果，以及田间条件的改变如何调整作物适宜性。对反事实模块在100个测试实例上的定量评估显示，其有效性率为94.60%，平均L1特征变化距离为0.18，每个场景平均修改2.10个特征——证实了所生成解释的实际可信度与稀疏性。上述功能通过交互式Streamlit网页仪表板实现，提供直观的实时反馈，使农民和农业从业者无需专业技术知识即可探索基于场景的决策。通过将高精度预测与以人为中心的可解释性整合于一个轻量级框架（模型大小4.3 MB，响应延迟187毫秒）中，KRISHI.AI推动了技术驱动型农业中可获取且可信赖的决策支持，尤其对服务印度小农户社区的农业决策支持应用具有重要价值。其中189.3毫秒对应专门的消融分析配置，而187毫秒则代表标准部署配置下的响应时间。","Scientific Reports",74,{"impact":19,"substance":186,"depth":19,"authority":187,"freshness":79,"relevant":22,"comment":188},22,14,"提出可解释的作物推荐框架，结合高精度与透明度，对农业AI落地有参考价值。",[190],{"name":183,"url":180},[27,28,30,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":195},{"candidates":196,"agricultural_evidence":47},[197,202,209,216],{"code":33,"score":17,"supported":47,"title":198,"abstract":200,"tags":201},[199],"machine learning",[50,51],[28,30],{"code":203,"score":99,"supported":47,"title":204,"abstract":205,"tags":208},"G2",[],[206,207],"小农户","smallholder",[],{"code":210,"score":99,"supported":47,"title":211,"abstract":212,"tags":215},"H2",[],[213,214],"土壤","soil",[],{"code":62,"score":22,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[27],[221,77],"农业人工智能 作物推荐 智慧农业 可解释AI","农业人工智能作物推荐智慧农业可解释AI-2005","10.1038\u002Fs41598-026-68815-w",{"doi":223,"openalex_id":225,"authors":226,"venue":183,"cited_by_count":88,"oa_url":180,"card":235,"direction":93,"ingested_from":96},"W7211943283",[227,230,232],{"name":228,"orcid":229},"Bhavya Dhingra","https:\u002F\u002Forcid.org\u002F0000-0002-9646-1869",{"name":231,"orcid":9},"Saarang Agarwal",{"name":233,"orcid":234},"Rishi Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1211-7989",{"tldr":236,"method":237,"finding":238,"direction":93,"opportunity":239},"提出可解释AI框架KRISHI.AI，基于土壤和气候数据推荐作物，并解释推荐原因。","LightGBM多分类模型结合SHAP特征归因和反事实分析，部署于Streaml","测试准确率99.39%，F1分数0.99，反事实有效性94.6%，模型轻量响应快。","可探索将可解释AI扩展到更多作物和地区，或集成实时传感器数据以增强动态推荐。","2026-09-09T23:30:37.344594Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":257,"secondary_topics":258,"classification":259,"search_phrases":295,"slug":298,"view_count":88,"doi":299,"paper":300,"created_at":318},5559,"Smart AI-Based Agriculture Decision Support System (AgriSens)","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85162","Traditional farming relies heavily on empirical guesswork and legacy ancestral knowledge, frequently leaving crops vulnerable to uncalibrated chemical fertilizer application, unpredictable micro-climates, and delayed foliar disease detection. This paper introduces AgriSens, a comprehensive, full-stack smart farming platform developed to empower farmers with datadriven precision agriculture. The system integrates a multi-parameter crop recommendation engine using a mathematical Gaussian-decay environmental fitness model alongside a multimodal leaf disease diagnostic system powered by Google Gemini 3.7 Flash computer vision to identify foliar pathogens from smartphone imagery. Furthermore, the architecture deploys a soiltexture-aware fertilizer optimization engine that calculates precise N-P-K (Nitrogen, Phosphorus, Potassium) nutrient deficits, a real-time agro-meteorological forecasting stream driven by the Open-Meteo API for chemical spray safety alerts, and a closedloop automated IoT irrigation telemetry simulator. To eliminate structural literacy barriers within rural farming communities, the platform features native voice synthesis and a 7-language localized translation module. Simulation benchmarks demonstrate that the platform achieves rapid visual diagnostic latencies under 2.5 seconds and optimizes water conservation by 25% to 35% compared to baseline scheduled flooding methods","传统农业严重依赖经验性猜测和祖辈传承的知识，作物常常面临化肥施用不当、微气候难以预测以及叶部病害发现滞后等问题。本文介绍AgriSens——一个综合性全栈智慧农业平台，旨在以数据驱动的精准农业赋能农民。该系统集成了基于高斯衰减环境适应度数学模型的多元参数作物推荐引擎，以及由Google Gemini 3.7 Flash计算机视觉驱动的多模态叶片病害诊断系统，可从智能手机图像中识别叶部病原体。此外，该架构还部署了土壤质地感知的肥料优化引擎，用于计算精确的氮磷钾（N-P-K）养分亏缺；由Open-Meteo API驱动的实时农业气象预报流，用于提供施药安全预警；以及闭环自动化物联网灌溉遥测模拟器。为消除农村农业社区中的结构性识字障碍，该平台具备原生语音合成功能和7种语言的本地化翻译模块。仿真基准测试表明，该平台实现了低于2.5秒的快速视觉诊断延迟，并且与基准定时漫灌方法相比，将节水效率优化了25%至35%。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":111,"substance":19,"depth":17,"authority":21,"freshness":13,"relevant":22,"comment":251},"论文提出集作物推荐、病害诊断、施肥优化与灌溉模拟于一体的全栈平台，方法有创新但尚属仿真验证阶段，具备一定参考价值。",[253],{"name":247,"url":244},[27,28,29,255,256],"智能灌溉","病害诊断",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":260},{"candidates":261,"agricultural_evidence":47},[262,269,273,277,281,285,289],{"code":33,"score":263,"supported":47,"title":264,"abstract":265,"tags":268},15,[51],[266,267],"计算机视觉","computer vision",[28],{"code":62,"score":13,"supported":47,"title":270,"abstract":271,"tags":272},[],[27,65,66],[27],{"code":54,"score":55,"supported":47,"title":274,"abstract":275,"tags":276},[],[58,59],[29],{"code":145,"score":55,"supported":47,"title":278,"abstract":279,"tags":280},[],[148,149],[255],{"code":152,"score":99,"supported":47,"title":282,"abstract":283,"tags":284},[],[155,156],[],{"code":210,"score":70,"supported":47,"title":286,"abstract":287,"tags":288},[],[213],[],{"code":290,"score":70,"supported":47,"title":291,"abstract":292,"tags":294},"H7",[],[293],"农业气象",[],[296,297],"AgriSens 智慧农业","Gemini 叶片病害诊断","AgriSens智慧农业-5559","10.22214\u002Fijraset.2026.85162",{"doi":299,"openalex_id":301,"authors":302,"venue":247,"cited_by_count":88,"oa_url":244,"card":313,"direction":95,"ingested_from":96},"W7223158459",[303,305,307,309,311],{"name":304,"orcid":9},"Harshini G. K.",{"name":306,"orcid":9},"Monisha B. P.",{"name":308,"orcid":9},"Manoj G.",{"name":310,"orcid":9},"Chandana K. M.",{"name":312,"orcid":9},"Prof. Sushma S.",{"tldr":314,"method":315,"finding":316,"direction":93,"opportunity":317},"构建AgriSens全栈智慧农业决策支持平台，集成作物推荐、病害诊断、施肥优化与灌溉模拟。","高斯衰减环境适应度模型、Gemini视觉诊断、Open-Meteo API与Io","视觉诊断延迟低于2.5秒，节水25%–35%，并支持7语言语音以降低识字门槛。","可验证多模态病害诊断在真实田间的准确率，并研究语音多语言交互对农户采纳行为的影响。","2026-10-22T11:20:18.985947Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":327,"score_detail":328,"sources":330,"tags":332,"primary_topic":334,"secondary_topics":335,"classification":336,"search_phrases":361,"slug":364,"view_count":88,"doi":365,"paper":366,"created_at":394},5080,"Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM","https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088467x261495708","Early and accurate detection of plant diseases is essential for improving crop productivity and supporting sustainable agricultural practices. Mango (Mangifera indica), one of the most economically important tropical fruit crops worldwide, is highly susceptible to several leaf diseases that can significantly reduce yield and fruit quality. This study presents an explainable deep learning framework for automated ten-class mango leaf disease classification using a publicly available dataset containing approximately 5000 images distributed across Anthracnose, Bacterial Canker, Cutting Weevil, Dag Disease, Die Back, Galls, Healthy, Powdery Mildew, Red Rust, and Sooty Mould classes. Unlike conventional AlexNet-based approaches, the proposed framework integrates Bayesian hyperparameter optimization, two-stage fine-tuning, test-time augmentation (TTA), and Grad-CAM-based explainability to improve both predictive performance and interpretability. Experimental results demonstrate that the proposed framework achieves a final test accuracy of 99.2% using eight-view test-time augmentation (TTA), with corresponding precision, recall, and F1-score of approximately 99.1%. The model also achieves micro-average and macro-average ROC–AUC scores of 1.00 on the curated test set, indicating strong discriminative capability across the considered disease categories. Comparative evaluations against MobileNetV2, DenseNet201, ConvNeXt, and LeafNet demonstrate competitive or superior classification performance while maintaining moderate computational complexity and architectural simplicity. Furthermore, Grad-CAM visualizations qualitatively highlight biologically relevant disease symptoms, including lesions, necrotic regions, rust spots, fungal growth, and tissue discoloration, thereby enhancing model interpretability. Although field-scale validation and quantitative explainability assessment remain subjects for future investigation, the proposed framework provides an accurate, computationally efficient, and interpretable approach for automated mango leaf disease diagnosis with strong potential for smart agriculture and precision farming applications.","植物病害的早期准确检测对于提高作物生产力和支持可持续农业实践至关重要。芒果（Mangifera indica）是全球最具经济重要性的热带水果作物之一，极易感染多种叶部病害，这些病害可显著降低产量和果实品质。本研究提出了一种可解释的深度学习框架，用于自动化十类芒果叶部病害分类，所使用的公开数据集包含约5000张图像，分布于炭疽病（Anthracnose）、细菌性溃疡病（Bacterial Canker）、切叶象甲（Cutting Weevil）、枯枝病（Dag Disease）、回枯病（Die Back）、虫瘿（Galls）、健康（Healthy）、白粉病（Powdery Mildew）、红锈病（Red Rust）和煤烟病（Sooty Mould）十个类别。与传统的基于AlexNet的方法不同，所提出的框架集成了贝叶斯超参数优化、两阶段微调、测试时增强（TTA）以及基于Grad-CAM的可解释性，以同时提升预测性能和可解释性。实验结果表明，所提出的框架在使用八视图测试时增强（TTA）时达到了99.2%的最终测试准确率，相应的精确率、召回率和F1分数约为99.1%。该模型在精选测试集上还取得了1.00的微平均和宏平均ROC–AUC分数，表明在所考虑的病害类别间具有较强的判别能力。与MobileNetV2、DenseNet201、ConvNeXt和LeafNet的对比评估表明，该框架在保持适中计算复杂度和架构简洁性的同时，具有竞争性或更优的分类性能。此外，Grad-CAM可视化定性地突出了具有生物学相关性的病害症状，包括病斑、坏死区域、锈斑、真菌生长和组织变色，从而增强了模型的可解释性。尽管田间规模验证和定量可解释性评估仍有待未来研究，但所提出的框架为自动化芒果叶部病害诊断提供了一种准确、计算高效且可解释的方法，在智慧农业和精准农业应用中具有强大潜力。","Intelligent Data Analysis","2026-10-15T00:00:00Z",76,{"impact":17,"substance":18,"depth":112,"authority":20,"freshness":21,"relevant":22,"comment":329},"将贝叶斯调参、两阶段微调与Grad-CAM结合用于十类芒果叶病识别，准确率99.2%，方法新颖且可解释性强，对智慧农业植保应用有参考价值。",[331],{"name":325,"url":322},[27,28,30,65,333],"芒果病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":337},{"candidates":338,"agricultural_evidence":47},[339,345,351,357],{"code":33,"score":17,"supported":47,"title":340,"abstract":342,"tags":344},[341],"deep learning",[343,341],"深度学习",[28,30],{"code":62,"score":346,"supported":47,"title":347,"abstract":348,"tags":350},11,[],[27,65,349],"smart agriculture",[27,65],{"code":352,"score":70,"supported":47,"title":353,"abstract":354,"tags":356},"B2",[],[355],"芒果",[],{"code":69,"score":70,"supported":47,"title":358,"abstract":359,"tags":360},[],[73],[],[362,363],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080","10.1177\u002F1088467x261495708",{"doi":365,"openalex_id":367,"authors":368,"venue":325,"cited_by_count":88,"oa_url":9,"card":389,"direction":95,"ingested_from":96},"W7222328160",[369,372,375,377,380,383,386],{"name":370,"orcid":371},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":373,"orcid":374},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":376,"orcid":9},"Md Moniruzzaman",{"name":378,"orcid":379},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":381,"orcid":382},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":384,"orcid":385},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":387,"orcid":388},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":390,"method":391,"finding":392,"direction":93,"opportunity":393},"提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。","AlexNet+贝叶斯超参优化、两阶段微调、八视图TTA、Grad-CAM，约5","测试准确率达99.2%，ROC-AUC为1.00，Grad-CAM能定位病斑等生物相关症状。","可延伸至田间复杂背景验证、可解释性定量评估及轻量化边缘部署研究。","2026-10-16T23:30:08.303796Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":401,"source_url":398,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":413,"search_phrases":440,"slug":443,"view_count":22,"doi":444,"paper":445,"created_at":460},5076,"Trustworthy Explainable AI for Lettuce Growth Stage Prediction Using Sensor Data in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1452.v1","Background In precision agriculture, especially in hydroponic systems where environmental management affects both production and resource efficiency, interpretable modeling of crop phenological stages is crucial. Typical visualization-based approaches are generally expensive and ecologically dependent. To classify and visualize lettuce growth stages (Seedling, Vegetative Growth, Heading, Bolting and Senescence) using temperature, humidity, total dissolved solids (TDS) and pH, this study proposed XGB-StageVision, a sensor-powered machine learning setup combining supervised and unsupervised learning techniques. Using XG Boost, LightGBM, CatBoost, ExtraTrees, Random Forest and LSTM under an 80\u002F20 train-test split, a dataset of 3,169 unique observations from 70 hydroponically grown lettuce plants was evaluated. Methods and Results XGB-StageVision achieved 99.86% test accuracy, a ROC AUC of 1.00, an MCC of 0.9982 and perfect precision, recall, F1 scores and specificity across all classes, performing competitively with the best baseline models, including LightGBM, which achieved statistically comparable cross validation performance. The unsupervised t-SNE visualization showed clearly distinct structural clusters across growth stages, while SHAP revealed temperature as the strongest predictor (≈75% contribution), followed by TDS. Novel Contributions SHAP, LIME and t-SNE provided global, local and structural interpretability and transparent perspectives on environmental influences regarding plant development. Statistical analysis and 10-fold cross-validation were used to further test the model’s robustness, reliability and generalizability. Conclusion XGB-StageVision provides an IoT-ready, interpretable and imaging-free system for monitoring lettuce growth stages in real time. The framework combines supervised predictive modeling with unsupervised structural visualization to ensure transparency, sustainability and high performance, supporting resource-efficient, data-driven strategic planning in contemporary hydroponic agriculture to lower monitoring costs, optimize inputs and improve yield consistency.","背景 在精准农业中，尤其是在环境管理同时影响产量和资源效率的水培系统中，作物物候阶段的可解释建模至关重要。典型的基于可视化的方法通常成本高昂且依赖生态条件。为了利用温度、湿度、总溶解固体（TDS）和pH对生菜生长阶段（幼苗期、营养生长期、结球期、抽薹期和衰老期）进行分类和可视化，本研究提出了XGB-StageVision，一种由传感器驱动的机器学习方案，结合了监督学习和无监督学习技术。在80\u002F20的训练-测试划分下，使用XGBoost、LightGBM、CatBoost、ExtraTrees、随机森林和LSTM，对来自70株水培生菜的3，169个独特观测值数据集进行了评估。方法与结果 XGB-StageVision达到了99.86%的测试准确率、1.00的ROC AUC、0.9982的MCC，并在所有类别上实现了完美的精确率、召回率、F1分数和特异性，与最佳基线模型表现相当，包括LightGBM，其交叉验证性能在统计上具有可比性。无监督t-SNE可视化显示了各生长阶段之间明显不同的结构聚类，而SHAP揭示温度是最强的预测因子（约75%贡献），其次是TDS。新颖贡献 SHAP、LIME和t-SNE提供了全局、局部和结构可解释性，以及关于环境对植物发育影响的可透明视角。使用统计分析和10折交叉验证进一步检验了模型的稳健性、可靠性和泛化性。结论 XGB-StageVision提供了一种物联网就绪、可解释且无需成像的系统，用于实时监测生菜生长阶段。该框架将监督预测建模与无监督结构可视化相结合，以确保透明度、可持续性和高性能，支持当代水培农业中资源高效、数据驱动的战略规划，以降低监测成本、优化投入并提高产量一致性。","Preprints.org",64,{"impact":111,"substance":404,"depth":112,"authority":99,"freshness":21,"relevant":22,"comment":405},20,"预印本论文，方法新颖、数据规模可观且强调可解释性，但尚未经同行评审，属细分领域技术进展。",[407],{"name":401,"url":398},[27,28,30,409,410],"水培生菜","传感器监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":414},{"candidates":415,"agricultural_evidence":47},[416,420,424,428,434],{"code":62,"score":263,"supported":47,"title":417,"abstract":418,"tags":419},[349],[65,66],[27],{"code":33,"score":46,"supported":47,"title":421,"abstract":422,"tags":423},[],[138,199],[28,30],{"code":152,"score":99,"supported":47,"title":425,"abstract":426,"tags":427},[],[155,156],[],{"code":429,"score":70,"supported":47,"title":430,"abstract":431,"tags":433},"B1",[],[432],"生菜",[],{"code":435,"score":70,"supported":47,"title":436,"abstract":437,"tags":439},"B7",[],[438],"hydroponic",[],[441,442],"XGB-StageVision 生菜 生长阶段","水培生菜 传感器 生长预测","XGB-StageVision生菜生长阶段-5076","10.20944\u002Fpreprints202610.1452.v1",{"doi":444,"openalex_id":446,"authors":447,"venue":401,"cited_by_count":88,"oa_url":398,"card":455,"direction":95,"ingested_from":96},"W7222080602",[448,450,453],{"name":449,"orcid":9},"Muhammad Majid",{"name":451,"orcid":452},"Attia Ghafoor","https:\u002F\u002Forcid.org\u002F0009-0000-3828-6479",{"name":454,"orcid":9},"Amna Munir",{"tldr":456,"method":457,"finding":458,"direction":95,"opportunity":459},"提出XGB-StageVision，用传感器数据与可解释AI预测水培生菜生长阶段。","XGBoost等模型结合SHAP、LIME、t-SNE，基于3169条温湿度TD","测试准确率99.86%，温度贡献约75%，t-SNE显示各生长阶段聚类清晰。","可探索多作物、多环境迁移与低成本传感器部署下的可解释模型泛化能力。","2026-10-16T23:30:08.020788Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":9,"source_name":183,"source_url":464,"published_at":467,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":468,"score_detail":469,"sources":471,"tags":473,"primary_topic":475,"secondary_topics":481,"classification":483,"search_phrases":502,"slug":505,"view_count":22,"doi":506,"paper":507,"created_at":532},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","2026-10-13T00:00:00Z",72,{"impact":111,"substance":404,"depth":19,"authority":187,"freshness":46,"relevant":22,"comment":470},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[472],{"name":183,"url":464},[27,28,30,293,474],"水稻产量预测",{"code":476,"slug":477,"name":478,"path":479,"parent_code":480},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[482],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":484},{"candidates":485,"agricultural_evidence":47},[486,490,494,498],{"code":33,"score":129,"supported":47,"title":487,"abstract":488,"tags":489},[199],[138,199,51],[28,30],{"code":476,"score":187,"supported":47,"title":491,"abstract":492,"tags":493},[477],[478,477],[],{"code":290,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[293],{"code":62,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[27],[503,504],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":506,"openalex_id":508,"authors":509,"venue":183,"cited_by_count":88,"oa_url":526,"card":527,"direction":95,"ingested_from":96},"W7221657053",[510,512,514,516,518,520,523],{"name":511,"orcid":9},"Noh-Hyun Lee",{"name":513,"orcid":9},"Wonjae Jeong",{"name":515,"orcid":9},"Hyeon-Ji Yang",{"name":517,"orcid":9},"Hana Cho",{"name":519,"orcid":9},"Hyeon-Yeong Ju",{"name":521,"orcid":522},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":524,"orcid":525},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":528,"method":529,"finding":530,"direction":93,"opportunity":531},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z"]