[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3274":3,"related-3274":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":74},3274,"Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42979-026-05329-2","Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility。SN Computer Science",null,"SN Computer Science","2026-09-22T00:00:00Z","论文",10,false,68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,9,1,"论文提出可解释猩猩优化算法驱动的土壤肥力决策支持系统，方法新颖且面向农户，但尚属学术探索阶段，产业影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","可解释AI","决策支持系统","土壤肥力",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":58},[38,47,53],{"code":39,"score":40,"supported":41,"title":42,"abstract":45,"tags":46},"I2",25,true,[43,44],"artificial intelligence","decision support",[43,44],[27,29,28],{"code":48,"score":16,"supported":41,"title":49,"abstract":51,"tags":52},"H2",[50],"soil",[50],[30],{"code":54,"score":21,"supported":13,"title":55,"abstract":56,"tags":57},"I1",[],[],[26],"农业相关性需要复核",[60,61],"农业人工智能 决策支持系统 土壤肥力 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统土壤肥力智慧农业-3274",0,"10.1007\u002Fs42979-026-05329-2",{"doi":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":63,"oa_url":8,"card":8,"direction":72,"ingested_from":73},"W7214018467",[68,70],{"name":69,"orcid":8},"K. Komala Devi",{"name":71,"orcid":8},"Josephine Prem Kumar","智慧农业 \u002F 农业物联网","openalex","2026-09-23T23:30:12.314548Z",{"total":76,"page":21,"page_size":76,"items":77},6,[78,150,196,274,346,422],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":8,"source_name":84,"source_url":81,"published_at":85,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":86,"score_detail":87,"sources":91,"tags":93,"primary_topic":96,"secondary_topics":101,"classification":102,"search_phrases":135,"slug":138,"view_count":63,"doi":139,"paper":140,"created_at":149},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":88,"substance":89,"depth":16,"authority":76,"freshness":20,"relevant":21,"comment":90},8,14,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[92],{"name":84,"url":81},[26,27,29,94,95],"病害识别","灌溉管理",{"code":39,"slug":97,"name":98,"path":99,"parent_code":100},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":103,"locked":13,"version":34,"method":35,"evidence":104},"classified",{"candidates":105,"agricultural_evidence":41},[106,111,119,124,131],{"code":39,"score":107,"supported":41,"title":108,"abstract":109,"tags":110},11,[44],[],[27,29,94],{"code":112,"score":113,"supported":41,"title":114,"abstract":115,"tags":118},"H3",7,[],[116,117],"灌溉","irrigation",[95],{"code":48,"score":76,"supported":41,"title":120,"abstract":121,"tags":123},[],[122,50],"土壤",[],{"code":125,"score":126,"supported":41,"title":127,"abstract":128,"tags":130},"F3",3,[],[129],"肥料",[],{"code":54,"score":21,"supported":13,"title":132,"abstract":133,"tags":134},[],[],[26],[136,137],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":139,"openalex_id":141,"authors":142,"venue":84,"cited_by_count":63,"oa_url":81,"card":143,"direction":72,"ingested_from":73},"W7222785888",[],{"tldr":144,"method":145,"finding":146,"direction":147,"opportunity":148},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","农业人工智能与决策模型","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":8,"source_name":156,"source_url":153,"published_at":157,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":63,"score_detail":158,"sources":160,"tags":162,"primary_topic":8,"secondary_topics":163,"classification":164,"search_phrases":174,"slug":176,"view_count":21,"doi":177,"paper":178,"created_at":195},5151,"XAI and its Applications in Decision Support System","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85188","Artificial Intelligence (AI) has transformed decision-making across various industries by enabling systems to analyse large volumes of data and provide intelligent recommendations. However, many modern AI models, particularly deep learning algorithms, function as \"black boxes,\" making it difficult for users to understand how decisions are generated. This lack of transparency limits the adoption of AI in critical domains such as healthcare, finance, law, and business. Explainable Artificial Intelligence (XAI) has emerged as a promising solution that improves transparency, interpretability, and trust in AI systems. Decision Support Systems (DSS) assist decision-makers by combining data, analytical models, and domain knowledge. Integrating XAI into DSS enables users to understand AI-generated recommendations, thereby improving confidence and accountability. This review paper explores the concepts of XAI, different explanation techniques, and their applications in DSS. The paper also discusses recent research, challenges, limitations, and future research directions","人工智能（Artificial Intelligence, AI）通过使系统能够分析大量数据并提供智能建议，已经改变了各行各业的决策方式。然而，许多现代AI模型，尤其是深度学习算法，作为“黑箱”运作，使用户难以理解决策是如何生成的。这种透明度的缺乏限制了AI在医疗、金融、法律和商业等关键领域的应用。可解释人工智能（Explainable Artificial Intelligence, XAI）作为一种有前景的解决方案应运而生，旨在提高AI系统的透明度、可解释性和信任度。决策支持系统（Decision Support Systems, DSS）通过结合数据、分析模型和领域知识来辅助决策者。将XAI集成到DSS中，使用户能够理解AI生成的建议，从而提高信心和问责性。本文综述探讨了XAI的概念、不同的解释技术及其在DSS中的应用。本文还讨论了近期研究、挑战、局限性以及未来研究方向。","International Journal for Research in Applied Science and Engineering Technology","2026-10-16T00:00:00Z",{"impact":63,"substance":63,"depth":63,"authority":63,"freshness":63,"relevant":63,"comment":159},"该文为通用XAI与决策支持系统综述，未涉及农业场景，与三农及农业信息化无直接关联，不建议进入每日精选。",[161],{"name":156,"url":153},[27,28,29],[],{"status":33,"locked":13,"version":34,"method":35,"evidence":165},{"candidates":166,"agricultural_evidence":13,"reason":58},[167],{"code":39,"score":168,"supported":41,"title":169,"abstract":170,"tags":173},20,[44],[171,172,43,44],"深度学习","deep learning",[27,29,28],[175,61],"农业人工智能 决策支持系统 可解释AI","农业人工智能决策支持系统可解释AI-5151","10.22214\u002Fijraset.2026.85188",{"doi":177,"openalex_id":179,"authors":180,"venue":156,"cited_by_count":63,"oa_url":153,"card":190,"direction":72,"ingested_from":73},"W7222727160",[181,183,185,188],{"name":182,"orcid":8},"Gitin Garg",{"name":184,"orcid":8},"Kiya Jain",{"name":186,"orcid":187},"Vishal Shrivastava","https:\u002F\u002Forcid.org\u002F0000-0002-8353-2752",{"name":189,"orcid":8},"Akhil Pandey",{"tldr":191,"method":192,"finding":193,"direction":147,"opportunity":194},"综述可解释AI技术及其在决策支持系统中的应用、挑战与未来方向。","文献综述，梳理XAI解释技术与DSS集成应用。","XAI能提升DSS透明度与信任，但应用仍面临挑战与局限。","可将XAI引入农业决策支持系统，研究面向农户的可解释推荐与信任机制。","2026-10-17T23:30:08.767848Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":8,"source_name":202,"source_url":199,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":204,"score_detail":205,"sources":209,"tags":211,"primary_topic":214,"secondary_topics":215,"classification":216,"search_phrases":240,"slug":243,"view_count":63,"doi":244,"paper":245,"created_at":273},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":18,"substance":206,"depth":207,"authority":19,"freshness":20,"relevant":21,"comment":208},21,17,"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。",[210],{"name":202,"url":199},[26,27,28,212,213],"精准农业","芒果病害",{"code":39,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":13,"version":34,"method":35,"evidence":217},{"candidates":218,"agricultural_evidence":41},[219,223,228,234],{"code":39,"score":18,"supported":41,"title":220,"abstract":221,"tags":222},[172],[171,172],[27,28],{"code":54,"score":107,"supported":41,"title":224,"abstract":225,"tags":227},[],[26,212,226],"smart agriculture",[26,212],{"code":229,"score":126,"supported":41,"title":230,"abstract":231,"tags":233},"B2",[],[232],"芒果",[],{"code":235,"score":126,"supported":41,"title":236,"abstract":237,"tags":239},"H8",[],[238],"可持续农业",[],[241,242],"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":244,"openalex_id":246,"authors":247,"venue":202,"cited_by_count":63,"oa_url":8,"card":268,"direction":72,"ingested_from":73},"W7222328160",[248,251,254,256,259,262,265],{"name":249,"orcid":250},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":252,"orcid":253},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":255,"orcid":8},"Md Moniruzzaman",{"name":257,"orcid":258},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":260,"orcid":261},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":263,"orcid":264},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":266,"orcid":267},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":269,"method":270,"finding":271,"direction":147,"opportunity":272},"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":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":8,"source_name":280,"source_url":277,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":325,"slug":328,"view_count":21,"doi":329,"paper":330,"created_at":345},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":16,"substance":168,"depth":207,"authority":76,"freshness":20,"relevant":21,"comment":283},"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。",[285],{"name":280,"url":277},[26,27,28,287,288],"水培生菜","传感器监测",{"code":39,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":13,"version":34,"method":35,"evidence":292},{"candidates":293,"agricultural_evidence":41},[294,300,306,313,319],{"code":54,"score":295,"supported":41,"title":296,"abstract":297,"tags":299},15,[226],[212,298],"precision agriculture",[26],{"code":39,"score":88,"supported":41,"title":301,"abstract":302,"tags":305},[],[303,304],"机器学习","machine learning",[27,28],{"code":307,"score":76,"supported":41,"title":308,"abstract":309,"tags":312},"I5",[],[310,311],"物联网","IoT",[],{"code":314,"score":126,"supported":41,"title":315,"abstract":316,"tags":318},"B1",[],[317],"生菜",[],{"code":320,"score":126,"supported":41,"title":321,"abstract":322,"tags":324},"B7",[],[323],"hydroponic",[],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":280,"cited_by_count":63,"oa_url":277,"card":340,"direction":72,"ingested_from":73},"W7222080602",[333,335,338],{"name":334,"orcid":8},"Muhammad Majid",{"name":336,"orcid":337},"Attia Ghafoor","https:\u002F\u002Forcid.org\u002F0009-0000-3828-6479",{"name":339,"orcid":8},"Amna Munir",{"tldr":341,"method":342,"finding":343,"direction":72,"opportunity":344},"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":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":8,"source_name":352,"source_url":349,"published_at":353,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":368,"classification":370,"search_phrases":391,"slug":394,"view_count":21,"doi":395,"paper":396,"created_at":421},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。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":16,"substance":168,"depth":17,"authority":89,"freshness":88,"relevant":21,"comment":356},"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。",[358],{"name":352,"url":349},[26,27,28,360,361],"农业气象","水稻产量预测",{"code":363,"slug":364,"name":365,"path":366,"parent_code":367},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[369],{"code":39,"slug":97,"name":98,"path":99,"parent_code":100},{"status":103,"locked":13,"version":34,"method":35,"evidence":371},{"candidates":372,"agricultural_evidence":41},[373,378,382,387],{"code":39,"score":374,"supported":41,"title":375,"abstract":376,"tags":377},19,[304],[303,304,44],[27,28],{"code":363,"score":89,"supported":41,"title":379,"abstract":380,"tags":381},[364],[365,364],[],{"code":383,"score":21,"supported":13,"title":384,"abstract":385,"tags":386},"H7",[],[],[360],{"code":54,"score":21,"supported":13,"title":388,"abstract":389,"tags":390},[],[],[26],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":352,"cited_by_count":63,"oa_url":415,"card":416,"direction":72,"ingested_from":73},"W7221657053",[399,401,403,405,407,409,412],{"name":400,"orcid":8},"Noh-Hyun Lee",{"name":402,"orcid":8},"Wonjae Jeong",{"name":404,"orcid":8},"Hyeon-Ji Yang",{"name":406,"orcid":8},"Hana Cho",{"name":408,"orcid":8},"Hyeon-Yeong Ju",{"name":410,"orcid":411},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":413,"orcid":414},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":417,"method":418,"finding":419,"direction":147,"opportunity":420},"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":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":8,"source_name":428,"source_url":425,"published_at":353,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":438,"search_phrases":454,"slug":457,"view_count":126,"doi":458,"paper":459,"created_at":470},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":88,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":430},"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。",[432],{"name":428,"url":425},[26,27,28,434,435],"植物病害识别","豌豆",{"code":39,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":13,"version":34,"method":35,"evidence":439},{"candidates":440,"agricultural_evidence":41},[441,445,449],{"code":39,"score":16,"supported":41,"title":442,"abstract":443,"tags":444},[],[171,172,43],[27,28,434],{"code":54,"score":113,"supported":41,"title":446,"abstract":447,"tags":448},[],[212,298],[26],{"code":450,"score":21,"supported":13,"title":451,"abstract":452,"tags":453},"A5",[],[],[435],[455,456],"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":458,"openalex_id":460,"authors":461,"venue":428,"cited_by_count":63,"oa_url":425,"card":465,"direction":147,"ingested_from":73},"W7221612639",[462],{"name":463,"orcid":464},"Fatma Ozge Ozkok","https:\u002F\u002Forcid.org\u002F0000-0002-1421-4670",{"tldr":466,"method":467,"finding":468,"direction":147,"opportunity":469},"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"]