[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2005":3,"related-2005":97},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":72,"slug":75,"view_count":20,"doi":76,"paper":77,"created_at":96},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。",null,"Scientific Reports","2026-09-09T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,2,1,"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。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业人工智能","可解释AI","作物推荐",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,52,60,67],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":51},16,true,[47],"machine learning",[49,50],"artificial intelligence","decision support",[27,28],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":59},"G2",6,[],[57,58],"小农户","smallholder",[],{"code":61,"score":54,"supported":45,"title":62,"abstract":63,"tags":66},"H2",[],[64,65],"土壤","soil",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"I1",[],[],[26],[73,74],"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","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":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7211943283",[80,83,85],{"name":81,"orcid":82},"Bhavya Dhingra","https:\u002F\u002Forcid.org\u002F0000-0002-9646-1869",{"name":84,"orcid":9},"Saarang Agarwal",{"name":86,"orcid":87},"Rishi Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1211-7989",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业人工智能与决策模型","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。","openalex","2026-09-09T23:30:37.344594Z",{"total":54,"page":21,"page_size":54,"items":98},[99,165,242,314,389,449],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":112,"tags":114,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":147,"slug":149,"view_count":20,"doi":150,"paper":151,"created_at":164},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。","Frontiers in Artificial Intelligence",77,{"impact":44,"substance":108,"depth":17,"authority":109,"freshness":110,"relevant":21,"comment":111},21,13,9,"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。",[113],{"name":105,"url":102},[26,27,115,28,29],"精准施肥",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":119},{"candidates":120,"agricultural_evidence":45},[121,126,134,140],{"code":31,"score":122,"supported":45,"title":123,"abstract":124,"tags":125},8,[],[49,50],[27,28],{"code":127,"score":128,"supported":45,"title":129,"abstract":130,"tags":133},"F3",7,[],[131,132],"肥料","fertilizer",[115],{"code":68,"score":128,"supported":45,"title":135,"abstract":136,"tags":139},[],[137,138],"精准农业","precision agriculture",[26],{"code":141,"score":142,"supported":45,"title":143,"abstract":144,"tags":146},"H8",3,[],[145],"可持续农业",[],[148,74],"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智慧农业精准施肥-2036","10.3389\u002Ffrai.2026.1910196",{"doi":150,"openalex_id":152,"authors":153,"venue":105,"cited_by_count":88,"oa_url":102,"card":158,"direction":163,"ingested_from":95},"W7211994206",[154,156],{"name":155,"orcid":9},"P. Latha",{"name":157,"orcid":9},"P. Kumaresan",{"tldr":159,"method":160,"finding":161,"direction":93,"opportunity":162},"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","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","2026-09-10T23:30:09.054372Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":208,"slug":211,"view_count":88,"doi":212,"paper":213,"created_at":241},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":44,"substance":108,"depth":175,"authority":109,"freshness":110,"relevant":21,"comment":176},17,"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。",[178],{"name":171,"url":168},[26,27,28,137,180],"芒果病害",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":184},{"candidates":185,"agricultural_evidence":45},[186,192,198,204],{"code":31,"score":44,"supported":45,"title":187,"abstract":189,"tags":191},[188],"deep learning",[190,188],"深度学习",[27,28],{"code":68,"score":193,"supported":45,"title":194,"abstract":195,"tags":197},11,[],[26,137,196],"smart agriculture",[26,137],{"code":199,"score":142,"supported":45,"title":200,"abstract":201,"tags":203},"B2",[],[202],"芒果",[],{"code":141,"score":142,"supported":45,"title":205,"abstract":206,"tags":207},[],[145],[],[209,210],"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":212,"openalex_id":214,"authors":215,"venue":171,"cited_by_count":88,"oa_url":9,"card":236,"direction":163,"ingested_from":95},"W7222328160",[216,219,222,224,227,230,233],{"name":217,"orcid":218},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":220,"orcid":221},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":223,"orcid":9},"Md Moniruzzaman",{"name":225,"orcid":226},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":228,"orcid":229},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":231,"orcid":232},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":234,"orcid":235},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":237,"method":238,"finding":239,"direction":93,"opportunity":240},"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":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":254,"tags":256,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":293,"slug":296,"view_count":21,"doi":297,"paper":298,"created_at":313},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":251,"substance":252,"depth":175,"authority":54,"freshness":110,"relevant":21,"comment":253},12,20,"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。",[255],{"name":248,"url":245},[26,27,28,257,258],"水培生菜","传感器监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":262},{"candidates":263,"agricultural_evidence":45},[264,269,274,281,287],{"code":68,"score":265,"supported":45,"title":266,"abstract":267,"tags":268},15,[196],[137,138],[26],{"code":31,"score":122,"supported":45,"title":270,"abstract":271,"tags":273},[],[272,47],"机器学习",[27,28],{"code":275,"score":54,"supported":45,"title":276,"abstract":277,"tags":280},"I5",[],[278,279],"物联网","IoT",[],{"code":282,"score":142,"supported":45,"title":283,"abstract":284,"tags":286},"B1",[],[285],"生菜",[],{"code":288,"score":142,"supported":45,"title":289,"abstract":290,"tags":292},"B7",[],[291],"hydroponic",[],[294,295],"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":297,"openalex_id":299,"authors":300,"venue":248,"cited_by_count":88,"oa_url":245,"card":308,"direction":163,"ingested_from":95},"W7222080602",[301,303,306],{"name":302,"orcid":9},"Muhammad Majid",{"name":304,"orcid":305},"Attia Ghafoor","https:\u002F\u002Forcid.org\u002F0009-0000-3828-6479",{"name":307,"orcid":9},"Amna Munir",{"tldr":309,"method":310,"finding":311,"direction":163,"opportunity":312},"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":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":10,"source_url":317,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":329,"secondary_topics":335,"classification":337,"search_phrases":358,"slug":361,"view_count":21,"doi":362,"paper":363,"created_at":388},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":251,"substance":252,"depth":17,"authority":19,"freshness":122,"relevant":21,"comment":323},"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。",[325],{"name":10,"url":317},[26,27,28,327,328],"农业气象","水稻产量预测",{"code":330,"slug":331,"name":332,"path":333,"parent_code":334},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[336],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":338},{"candidates":339,"agricultural_evidence":45},[340,345,349,354],{"code":31,"score":341,"supported":45,"title":342,"abstract":343,"tags":344},19,[47],[272,47,50],[27,28],{"code":330,"score":19,"supported":45,"title":346,"abstract":347,"tags":348},[331],[332,331],[],{"code":350,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},"H7",[],[],[327],{"code":68,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[26],[359,360],"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":362,"openalex_id":364,"authors":365,"venue":10,"cited_by_count":88,"oa_url":382,"card":383,"direction":163,"ingested_from":95},"W7221657053",[366,368,370,372,374,376,379],{"name":367,"orcid":9},"Noh-Hyun Lee",{"name":369,"orcid":9},"Wonjae Jeong",{"name":371,"orcid":9},"Hyeon-Ji Yang",{"name":373,"orcid":9},"Hana Cho",{"name":375,"orcid":9},"Hyeon-Yeong Ju",{"name":377,"orcid":378},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":380,"orcid":381},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":384,"method":385,"finding":386,"direction":93,"opportunity":387},"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",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":426,"slug":429,"view_count":20,"doi":430,"paper":431,"created_at":448},4990,"Smart Crop Advisory System","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i10.081","The Smart Crop Advisory System is a web-based agricultural decision-support platform that uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to assist farmers in making informed farming decisions. Traditional agriculture faces several challenges, including unsuitable crop selection, unpredictable weather conditions, crop diseases, and fluctuations in market prices. The proposed system integrates essential agricultural services, including crop recommendation, weather forecasting, crop disease detection, agricultural market price information, and an AI-based multilingual voice assistant. It also provides farmer profile management, crop price trend analysis, and rainfall trend visualization through an interactive dashboard. The crop recommendation module helps farmers identify suitable crops based on relevant agricultural parameters, while the disease detection module supports the identification of potential crop diseases. Weather and market information help farmers plan agricultural activities and make informed selling decisions. The multilingual voice assistant aims to improve accessibility by enabling farmers to interact with the system through voice-based communication. Overall, the Smart Crop Advisory System aims to improve access to agricultural information, support effective decision-making, promote efficient resource utilization, and encourage sustainable farming practices. Keywords: Smart Agriculture, Crop Recommendation, Machine Learning, Crop Disease Detection, Weather Forecasting, Market Price Analysis, Voice Assistant, Sustainable Agriculture.","智能作物咨询系统是一个基于网络的农业决策支持平台，利用人工智能（AI）和机器学习（ML）技术帮助农民做出明智的农作决策。传统农业面临诸多挑战，包括作物选择不当、天气条件不可预测、作物病害以及市场价格波动。所提出的系统整合了基本农业服务，包括作物推荐、天气预报、作物病害检测、农产品市场价格信息以及基于AI的多语言语音助手。它还通过交互式仪表板提供农民档案管理、作物价格趋势分析和降雨趋势可视化。作物推荐模块帮助农民根据相关农业参数识别适宜作物，而病害检测模块支持识别潜在作物病害。天气和市场信息帮助农民规划农业活动并做出明智的销售决策。多语言语音助手旨在通过使农民能够以语音方式与系统交互来提高可及性。总体而言，智能作物咨询系统旨在改善农业信息的获取，支持有效决策，促进资源高效利用，并鼓励可持续农业实践。关键词：智慧农业、作物推荐、机器学习、作物病害检测、天气预报、市场价格分析、语音助手、可持续农业。","International Journal of Creative and Open Research in Engineering and Management","2026-10-14T00:00:00Z",48,{"impact":122,"substance":19,"depth":251,"authority":54,"freshness":122,"relevant":21,"comment":399},"面向农户的AI农业决策支持系统论文，功能集成度较高但方法新颖性与数据规模有限，属细分领域进展，可作为智慧农业主题聚合素材。",[401],{"name":395,"url":392},[26,27,403,327,29],"病虫害识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":407},{"candidates":408,"agricultural_evidence":45},[409,413,417,422],{"code":31,"score":193,"supported":45,"title":410,"abstract":411,"tags":412},[],[272,47,49],[27,403],{"code":68,"score":128,"supported":45,"title":414,"abstract":415,"tags":416},[],[26,196],[26],{"code":141,"score":54,"supported":45,"title":418,"abstract":419,"tags":421},[],[145,420],"sustainable agriculture",[],{"code":350,"score":21,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[327],[427,428],"AI 作物推荐 语音助手","农业人工智能 病虫害识别 作物推荐 农业气象","AI作物推荐语音助手-4990","10.55041\u002Fijcope.v2i10.081",{"doi":430,"openalex_id":432,"authors":433,"venue":395,"cited_by_count":88,"oa_url":442,"card":443,"direction":163,"ingested_from":95},"W7221803029",[434,436,438,440],{"name":435,"orcid":9},"Bhuvana H P",{"name":437,"orcid":9},"Kavya C M",{"name":439,"orcid":9},"Manjula S",{"name":441,"orcid":9},"Priyanka A","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002FSmart-Crop-Advisory-System.pdf",{"tldr":444,"method":445,"finding":446,"direction":93,"opportunity":447},"构建基于AI\u002FML的Web农业决策支持平台，为农民提供作物推荐、病害检测、天气与市场价格等服务。","集成机器学习模型、多语言语音助手与交互式仪表盘，整合天气、市场及农业参数数据。","系统可提升农业信息获取与决策效率，促进资源高效利用和可持续耕作。","可探索多源数据融合的轻量化模型与本地化语音交互，提升小农户采纳率与实时决策精度。","2026-10-15T23:30:09.048921Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":456,"sources":458,"tags":460,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":481,"slug":484,"view_count":142,"doi":485,"paper":486,"created_at":497},4907,"PEAS-DeiT: Multi-Scale Spatial Attention Enhanced DeiT for Pea Leaf Disease Classification","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp162010113","Early and accurate detection of pea diseases is essential for reducing crop losses and supporting precision agriculture. Although deep learning methods have shown good performance in plant disease classification, CNN-based models may have limitations in learning long-range relationships and detailed disease features. In this study, an explainable transformer-based framework, named PEAS-DeiT, is proposed for automated pea leaf disease classification. The proposed approach integrates a multi-scale spatial attention mechanism into the Data-efficient Image Transformer (DeiT) architecture to enhance the representation of disease-related visual features. A unified pea leaf disease dataset (UPLDD) was constructed by combining multiple publicly available datasets, covering six classes, including five disease categories and healthy samples. The proposed model was evaluated against CNN-based and transformer-based architectures using five-fold cross-validation. Furthermore, explainable artificial intelligence (XAI) analysis was performed to investigate the regions contributing to model predictions. Experimental results demonstrate that PEAS-DeiT achieved an accuracy of 93.11%, showing competitive performance compared with the evaluated CNN and transformer-based models. The gradient-based saliency analysis also revealed localized response patterns in several evaluated samples, providing qualitative insight into the image regions contributing to model predictions. Statistical analyses were conducted to evaluate the performance differences among the compared models. These findings indicate that PEAS-DeiT provides a competitive approach for pea leaf disease classification within the evaluated experimental setting.","豌豆病害的早期准确检测对于减少作物损失和支持精准农业至关重要。尽管深度学习方法在植物病害分类中表现出良好性能，但基于卷积神经网络（CNN）的模型在学习长程关系和病害细节特征方面可能存在局限。本研究提出了一种基于Transformer的可解释框架PEAS-DeiT，用于豌豆叶片病害的自动化分类。该方法将多尺度空间注意力机制集成到数据高效图像Transformer（DeiT）架构中，以增强病害相关视觉特征的表征能力。通过整合多个公开数据集，构建了统一的豌豆叶片病害数据集（UPLDD），涵盖六个类别，包括五类病害和健康样本。采用五折交叉验证，将所提模型与基于CNN和基于Transformer的架构进行了评估比较。此外，进行了可解释人工智能（XAI）分析，以探究对模型预测有贡献的区域。实验结果表明，PEAS-DeiT达到了93.11%的准确率，与所评估的基于CNN和基于Transformer的模型相比展现出有竞争力的性能。基于梯度的显著性分析还揭示了若干评估样本中的局部响应模式，为理解对模型预测有贡献的图像区域提供了定性见解。通过统计分析评估了所比较模型之间的性能差异。这些发现表明，在所评估的实验设置下，PEAS-DeiT为豌豆叶片病害分类提供了一种有竞争力的方法。","Applied Sciences",{"impact":122,"substance":17,"depth":44,"authority":109,"freshness":110,"relevant":21,"comment":457},"提出多尺度空间注意力增强的DeiT模型用于豌豆叶病害分类，构建六类统一数据集并达93.11%准确率，方法有创新但属实验室验证阶段，产业影响有限。",[459],{"name":455,"url":452},[26,27,28,461,462],"植物病害识别","豌豆",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":466},{"candidates":467,"agricultural_evidence":45},[468,472,476],{"code":31,"score":251,"supported":45,"title":469,"abstract":470,"tags":471},[],[190,188,49],[27,28,461],{"code":68,"score":128,"supported":45,"title":473,"abstract":474,"tags":475},[],[137,138],[26],{"code":477,"score":21,"supported":14,"title":478,"abstract":479,"tags":480},"A5",[],[],[462],[482,483],"PEAS-DeiT 豌豆叶病害","DeiT 植物病害分类","PEAS-DeiT豌豆叶病害-4907","10.3390\u002Fapp162010113",{"doi":485,"openalex_id":487,"authors":488,"venue":455,"cited_by_count":88,"oa_url":452,"card":492,"direction":93,"ingested_from":95},"W7221612639",[489],{"name":490,"orcid":491},"Fatma Ozge Ozkok","https:\u002F\u002Forcid.org\u002F0000-0002-1421-4670",{"tldr":493,"method":494,"finding":495,"direction":93,"opportunity":496},"提出PEAS-DeiT模型，将多尺度空间注意力融入DeiT，实现豌豆叶片病害分类。","多尺度空间注意力增强DeiT，构建UPLDD数据集，五折交叉验证与XAI分析。","PEAS-DeiT达到93.11%准确率，优于对比的CNN和Transformer模型。","可探索轻量化Transformer在移动端病害识别中的部署，并扩展多作物跨域泛化能力。","2026-10-14T23:30:46.091813Z"]