[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5080":3,"related-5080":110},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":109},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。",null,"Intelligent Data Analysis","2026-10-15T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","可解释AI","精准农业","芒果病害",{"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":46},[45,52,59,66],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":51},true,[48],"deep learning",[50,48],"深度学习",[28,29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"I1",11,[],[27,30,57],"smart agriculture",[27,30],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":65},"B2",3,[],[64],"芒果",[],{"code":67,"score":61,"supported":46,"title":68,"abstract":69,"tags":71},"H8",[],[70],"可持续农业",[],[73,74],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080",0,"10.1177\u002F1088467x261495708",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":76,"oa_url":9,"card":101,"direction":107,"ingested_from":108},"W7222328160",[81,84,87,89,92,95,98],{"name":82,"orcid":83},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":85,"orcid":86},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":88,"orcid":9},"Md Moniruzzaman",{"name":90,"orcid":91},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":93,"orcid":94},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":96,"orcid":97},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":99,"orcid":100},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:08.303796Z",{"total":111,"page":22,"page_size":111,"items":112},6,[113,177,238,358,409,496],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":122,"score_detail":123,"sources":128,"tags":132,"primary_topic":134,"secondary_topics":135,"classification":136,"search_phrases":154,"slug":157,"view_count":158,"doi":159,"paper":160,"created_at":176},4293,"Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23159807","Abstract: Today, the challenges in agriculture are so big and complicated that agricultural professionals can't just look at them and figure them out. This study looks at the newest deep learning and computer vision techniques for finding and sorting mango leaf diseases. Leaf diseases cut the quality and quantity of mango crops by 30–80%, which costs the worldwide mango industry billions of dollars. We meticulously examine numerous scholarly articles on deep neural networks and image processing. We categorize current methodologies into three technical generations: conventional image processing systems, first deep learning applications, and advanced hybrid architectures that incorporate transfer learning, attention mechanisms, and explainable AI. We systematically assess performance metrics, dataset characteristics, and implementation challenges across diverse geographical regions. Our comprehensive evaluation framework and prospective research trajectories underscore sustainable, scalable, and farmer-centric solutions. This review is a complete guide for researchers studying tropical fruit and people who make agricultural technologies. This study fills in the gaps in research on dataset standardization, model interpretability, real-time deployment, and the integration of precision agriculture systems. Keywords: Smart Farming, Deep Learning, Mango Diseases, Computer Vision, Precision Agriculture, Transfer Learning. Title: Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production Author: Shivam Saini, Rohit Goyal, Rakesh Arya International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 4, October 2026 - December 2026 Page No: 8-13 Research Publish Journals Website: www.researchpublish.com Published Date: 05-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23159808 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fdeep-learning-for-early-disease-identification-in-leaves-a-pathway-to-sustainable-mango-production","摘要：如今，农业面临的挑战如此巨大而复杂，农业专业人员仅凭观察已无法应对。本研究探讨了用于发现和分类芒果叶片病害的最新深度学习与计算机视觉技术。叶片病害使芒果作物的质量和产量下降30%–80%，给全球芒果产业造成数十亿美元的损失。我们细致梳理了大量关于深度神经网络和图像处理的学术论文。我们将当前方法划分为三个技术世代：传统图像处理系统、第一代深度学习应用，以及融合迁移学习、注意力机制和可解释人工智能的先进混合架构。我们系统评估了不同地理区域的性能指标、数据集特征和实施挑战。我们的综合评估框架和前瞻性研究路径强调了可持续、可扩展且以农户为中心的解决方案。本综述为研究热带水果的研究人员以及农业技术开发者提供了完整指南。本研究填补了数据集标准化、模型可解释性、实时部署以及精准农业系统集成方面的研究空白。关键词：智慧农业、深度学习、芒果病害、计算机视觉、精准农业、迁移学习。标题：深度学习用于叶片早期病害识别：通往可持续芒果生产的路径。作者：Shivam Saini，Rohit Goyal，Rakesh Arya。《国际计算机科学与信息技术研究杂志》，ISSN 2348-1196（印刷版），ISSN 2348-120X（在线版），第14卷，第4期，2026年10月–2026年12月，页码：8-13。Research Publish Journals网站：www.researchpublish.com。发布日期：2026年10月5日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23159808。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fdeep-learning-for-early-disease-identification-in-leaves-a-pathway-to-sustainable-mango-production","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,60,{"impact":124,"substance":125,"depth":126,"authority":111,"freshness":21,"relevant":22,"comment":127},12,18,15,"综述性论文，系统梳理芒果叶病深度学习识别技术代际与瓶颈，对热带果树智慧植保有参考价值，但期刊层级与影响范围有限。",[129,130],{"name":119,"url":116},{"name":119,"url":131},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23159808",[27,28,133,30,31],"计算机视觉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":46},[139,145,150],{"code":33,"score":140,"supported":46,"title":141,"abstract":142,"tags":144},19,[48],[50,133,48,143],"computer vision",[28,133],{"code":53,"score":54,"supported":46,"title":146,"abstract":147,"tags":149},[],[27,30,148],"precision agriculture",[27,30],{"code":60,"score":61,"supported":46,"title":151,"abstract":152,"tags":153},[],[64],[],[155,156],"芒果叶片病害 深度学习","芒果病害 计算机视觉","芒果叶片病害深度学习-4293",2,"10.5281\u002Fzenodo.23159807",{"doi":159,"openalex_id":161,"authors":162,"venue":119,"cited_by_count":76,"oa_url":116,"card":171,"direction":107,"ingested_from":108},"W7220380038",[163,166,168],{"name":164,"orcid":165},"Shivam Saini","https:\u002F\u002Forcid.org\u002F0009-0002-3981-2260",{"name":167,"orcid":9},"Rohit Goyal",{"name":169,"orcid":170},"Rakesh Arya","https:\u002F\u002Forcid.org\u002F0000-0003-2436-3060",{"tldr":172,"method":173,"finding":174,"direction":105,"opportunity":175},"综述深度学习与计算机视觉在芒果叶片病害早期识别中的应用，梳理三代技术并展望可持续种植。","系统综述深度神经网络与图像处理文献，按三代技术分类并评估性能、数据集与落地挑战。","叶片病害致芒果减产30–80%，需标准化数据集、可解释模型与实时部署。","芒果等热带果树病害数据集标准化与轻量化实时模型、可解释AI的田间落地研究仍空白。","2026-10-06T23:30:07.644270Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":9,"content":9,"source_name":182,"source_url":180,"published_at":183,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":184,"score_detail":185,"sources":188,"tags":193,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":214,"slug":217,"view_count":218,"doi":219,"paper":220,"created_at":237},3565,"An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability","https:\u002F\u002Fdoi.org\u002F10.7759\u002Fs44389-026-00295-5","An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.","Cureus Journal of Computer Science.","2026-09-25T00:00:00Z",39,{"impact":186,"substance":111,"depth":13,"authority":111,"freshness":21,"relevant":22,"comment":187},8,"主题契合智慧农业与农业AI，但摘要仅重复标题、无方法与数据细节，信息增量有限，暂不建议进入每日精选。",[189,190],{"name":182,"url":180},{"name":191,"url":192},"Cureus Journal of Computer Science 2026-09-25","https:\u002F\u002Fwww.cureusjournals.com\u002Farticles\u002F20543",[27,28,29,30,194],"气候适应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":198},{"candidates":199,"agricultural_evidence":46},[200,204,209],{"code":53,"score":20,"supported":46,"title":201,"abstract":202,"tags":203},[148],[148],[27,30],{"code":33,"score":20,"supported":46,"title":205,"abstract":207,"tags":208},[206],"artificial intelligence",[206],[28,29],{"code":210,"score":22,"supported":14,"title":211,"abstract":212,"tags":213},"H7",[],[],[194],[215,216],"气候适应 精准农业 可解释AI","农业人工智能 智慧农业 气候适应 精准农业","气候适应精准农业可解释AI-3565",5,"10.7759\u002Fs44389-026-00295-5",{"doi":219,"openalex_id":221,"authors":222,"venue":182,"cited_by_count":76,"oa_url":180,"card":9,"direction":105,"ingested_from":108},"W7214363342",[223,226,229,231,233,235],{"name":224,"orcid":225},"Mritunjay Kr. Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-0240-4909",{"name":227,"orcid":228},"Rohit Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4436-8275",{"name":230,"orcid":9},"Nitin  D Mali",{"name":232,"orcid":9},"Ansh  A Rajore",{"name":234,"orcid":9},"Gaurav Narendra Patil",{"name":236,"orcid":9},"Ankita  N Patil","2026-09-26T23:30:47.893666Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":246,"sources":249,"tags":251,"primary_topic":253,"secondary_topics":254,"classification":265,"search_phrases":342,"slug":345,"view_count":158,"doi":346,"paper":347,"created_at":357},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",{"impact":125,"substance":247,"depth":19,"authority":20,"freshness":186,"relevant":22,"comment":248},20,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[250],{"name":244,"url":241},[27,28,252,29,30],"产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[255,261],{"code":256,"slug":257,"name":258,"path":259,"parent_code":260},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":67,"slug":262,"name":263,"path":264,"parent_code":260},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":40,"locked":14,"version":41,"method":42,"evidence":266},{"candidates":267,"agricultural_evidence":46},[268,275,281,286,293,300,307,314,321,327,333,338],{"code":33,"score":54,"supported":46,"title":269,"abstract":270,"tags":274},[],[271,272,133,273,206,143],"目标检测","机器学习","machine learning",[28,29],{"code":256,"score":21,"supported":46,"title":276,"abstract":277,"tags":280},[],[278,279,257],"灌溉","水资源",[],{"code":67,"score":186,"supported":46,"title":282,"abstract":284,"tags":285},[283],"sustainable agriculture",[],[],{"code":287,"score":111,"supported":46,"title":288,"abstract":289,"tags":292},"A8",[],[290,291],"粮食安全","food security",[],{"code":294,"score":111,"supported":46,"title":295,"abstract":296,"tags":299},"F3",[],[297,298],"肥料","fertilizer",[],{"code":301,"score":111,"supported":46,"title":302,"abstract":303,"tags":306},"H2",[],[304,305],"土壤","soil",[],{"code":308,"score":111,"supported":46,"title":309,"abstract":310,"tags":313},"I5",[],[311,312],"物联网","internet of things",[],{"code":315,"score":316,"supported":46,"title":317,"abstract":318,"tags":320},"I3",4,[],[319],"遥感",[252],{"code":322,"score":61,"supported":46,"title":323,"abstract":324,"tags":326},"A7",[],[325],"间作",[],{"code":328,"score":61,"supported":46,"title":329,"abstract":330,"tags":332},"F4",[],[331],"除草",[],{"code":210,"score":61,"supported":46,"title":334,"abstract":335,"tags":337},[],[336],"气候变化",[],{"code":53,"score":158,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[27,30],[343,344],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":346,"openalex_id":348,"authors":349,"venue":244,"cited_by_count":76,"oa_url":241,"card":352,"direction":107,"ingested_from":108},"W7212616413",[350],{"name":351,"orcid":9},"Manish Maan",{"tldr":353,"method":354,"finding":355,"direction":105,"opportunity":356},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":361,"published_at":365,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":366,"score_detail":367,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":375,"classification":376,"search_phrases":391,"slug":394,"view_count":76,"doi":395,"paper":396,"created_at":408},2178,"Hybrid explainable DeiT-based framework for plant disease classification and severity estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02191-2","The detection of plant diseases is essential for preserving agricultural productivity and food security; however, existing approaches often suffer from limited interpretability and generalization capability. This study proposes a hybrid deep learning framework based on Data-efficient Image Transformers (DeiT) for plant disease classification and severity estimation. The framework employs DeiT-Base, DeiT-Small, and DeiT-Tiny models to capture global contextual dependencies in plant leaf images. To improve interpretability, a hybrid Explainable Artificial Intelligence (XAI) module is introduced by combining Gradient-weighted Class Activation Mapping (Grad-CAM) for local feature attribution with Attention Rollout for global dependency visualization. In addition, HSV-based segmentation is applied after classification to isolate disease-relevant regions for damage ratio computation, severity estimation, and explanation refinement. The damage ratio is further integrated with Hybrid XAI attention maps to estimate disease severity. Experiments were conducted on the New Plant Diseases Dataset (Augmented), comprising 70,295 training images and 17,572 validation images across 38 disease classes and 14 plant species. The proposed DeiT-Base model achieved a maximum classification accuracy of 99.13%, outperforming several CNN architectures, including ResNet50, DenseNet121, MobileNetV3, EfficientNet-B4, and InceptionV3. Furthermore, the proposed Hybrid XAI framework demonstrated superior interpretability performance in terms of Focus Score, Background Noise, Signal-to-Noise Ratio (SNR), and Entropy compared with individual explanation methods. Overall, the proposed framework improves classification accuracy, enhances model transparency, and provides meaningful disease severity estimation, making it a promising solution for intelligent precision agriculture.","植物病害检测对于保障农业生产力和粮食安全至关重要，然而现有方法往往存在可解释性有限和泛化能力不足的问题。本研究提出了一种基于数据高效图像Transformer（Data-efficient Image Transformers，DeiT）的混合深度学习框架，用于植物病害分类和严重程度估计。该框架采用DeiT-Base、DeiT-Small和DeiT-Tiny模型来捕获植物叶片图像中的全局上下文依赖关系。为提高可解释性，引入了一种混合可解释人工智能（Explainable Artificial Intelligence，XAI）模块，将用于局部特征归因的梯度加权类激活映射（Gradient-weighted Class Activation Mapping，Grad-CAM）与用于全局依赖可视化的注意力展开（Attention Rollout）相结合。此外，在分类之后应用基于HSV的分割来分离病害相关区域，以进行损伤比率计算、严重程度估计和解释优化。损伤比率进一步与混合XAI注意力图相结合以估计病害严重程度。实验在新植物病害数据集（增强版）（New Plant Diseases Dataset (Augmented)）上进行，该数据集包含70,295张训练图像和17,572张验证图像，涵盖38个病害类别和14种植物物种。所提出的DeiT-Base模型达到了99.13%的最高分类准确率，优于多种CNN架构，包括ResNet50、DenseNet121、MobileNetV3、EfficientNet-B4和InceptionV3。此外，所提出的混合XAI框架在聚焦分数（Focus Score）、背景噪声（Background Noise）、信噪比（Signal-to-Noise Ratio，SNR）和熵（Entropy）方面表现出优于单一解释方法的可解释性性能。总体而言，所提出的框架提高了分类准确率，增强了模型透明度，并提供了有意义的病害严重程度估计，使其成为智能精准农业的一种有前景的解决方案。","Discover Artificial Intelligence","2026-09-10T00:00:00Z",78,{"impact":125,"substance":368,"depth":125,"authority":124,"freshness":186,"relevant":22,"comment":369},22,"基于DeiT与混合可解释AI的植物病害分类与严重度估计研究，在7万张图像、38类病害上取得99.13%准确率，方法新颖、数据扎实，对智慧农业病害智能诊断有参考价值。",[371],{"name":364,"url":361},[27,28,29,30,373],"植物病害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":377},{"candidates":378,"agricultural_evidence":46},[379,383,387],{"code":33,"score":124,"supported":46,"title":380,"abstract":381,"tags":382},[],[50,48,206],[28,29,373],{"code":53,"score":186,"supported":46,"title":384,"abstract":385,"tags":386},[],[30,148],[27,30],{"code":287,"score":111,"supported":46,"title":388,"abstract":389,"tags":390},[],[290,291],[],[392,393],"农业人工智能 植物病害识别 智慧农业 精准农业","农业人工智能 植物病害识别","农业人工智能植物病害识别智慧农业精准农业-2178","10.1007\u002Fs44163-026-02191-2",{"doi":395,"openalex_id":397,"authors":398,"venue":364,"cited_by_count":76,"oa_url":361,"card":403,"direction":105,"ingested_from":108},"W7212179384",[399,401],{"name":400,"orcid":9},"Muskan Batra",{"name":402,"orcid":9},"Pooja Sharma",{"tldr":404,"method":405,"finding":406,"direction":105,"opportunity":407},"提出基于DeiT的混合可解释框架，实现植物病害分类与严重度估计。","DeiT-Base\u002FSmall\u002FTiny结合Grad-CAM与Attention","DeiT-Base分类准确率达99.13%，混合XAI可解释性指标优于单一方法。","可探索轻量化DeiT在边缘设备部署及多模态数据融合的病害严重度实时估计。","2026-09-11T23:30:47.756846Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":424,"secondary_topics":425,"classification":431,"search_phrases":472,"slug":475,"view_count":76,"doi":476,"paper":477,"created_at":495},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":125,"substance":368,"depth":125,"authority":20,"freshness":21,"relevant":22,"comment":419},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[421],{"name":415,"url":412},[27,28,311,30,423],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[426,430],{"code":308,"slug":427,"name":428,"path":429,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":256,"slug":257,"name":258,"path":259,"parent_code":260},{"status":40,"locked":14,"version":41,"method":42,"evidence":432},{"candidates":433,"agricultural_evidence":46},[434,438,442,449,453,459,463,467],{"code":33,"score":125,"supported":46,"title":435,"abstract":436,"tags":437},[206],[50,272,133,273,48,143],[28],{"code":53,"score":17,"supported":46,"title":439,"abstract":440,"tags":441},[148],[30,148],[27,30],{"code":308,"score":13,"supported":46,"title":443,"abstract":444,"tags":448},[],[311,445,446,447],"边缘计算","IoT","edge computing",[311],{"code":256,"score":21,"supported":46,"title":450,"abstract":451,"tags":452},[],[278,279,257],[],{"code":210,"score":454,"supported":46,"title":455,"abstract":456,"tags":458},7,[],[336,457],"climate change",[423],{"code":301,"score":111,"supported":46,"title":460,"abstract":461,"tags":462},[],[304,305],[],{"code":322,"score":61,"supported":46,"title":464,"abstract":465,"tags":466},[],[325],[],{"code":294,"score":61,"supported":46,"title":468,"abstract":469,"tags":471},[],[470],"养分管理",[],[473,474],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":476,"openalex_id":478,"authors":479,"venue":415,"cited_by_count":76,"oa_url":412,"card":490,"direction":107,"ingested_from":108},"W7223121456",[480,483,485,487],{"name":481,"orcid":482},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":484,"orcid":9},"Oluwadare OYEBODE",{"name":486,"orcid":9},"Iretiayo Adelaiye",{"name":488,"orcid":489},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":491,"method":492,"finding":493,"direction":105,"opportunity":494},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":497,"title":498,"url":499,"summary":500,"summary_zh":501,"content":9,"source_name":502,"source_url":499,"published_at":503,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":504,"score_detail":505,"sources":509,"tags":511,"primary_topic":513,"secondary_topics":514,"classification":515,"search_phrases":538,"slug":541,"view_count":316,"doi":542,"paper":543,"created_at":569},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":506,"substance":507,"depth":140,"authority":126,"freshness":21,"relevant":22,"comment":508},26,24,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[510],{"name":502,"url":499},[27,28,30,319,512],"全球评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":516},{"candidates":517,"agricultural_evidence":46},[518,522,526,530,534],{"code":53,"score":54,"supported":46,"title":519,"abstract":520,"tags":521},[],[27,30,148],[27,30],{"code":33,"score":454,"supported":46,"title":523,"abstract":524,"tags":525},[],[272,273],[28],{"code":256,"score":111,"supported":46,"title":527,"abstract":528,"tags":529},[],[278,257],[],{"code":315,"score":316,"supported":46,"title":531,"abstract":532,"tags":533},[],[319],[319],{"code":328,"score":61,"supported":46,"title":535,"abstract":536,"tags":537},[],[331],[],[539,540],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":542,"openalex_id":544,"authors":545,"venue":502,"cited_by_count":76,"oa_url":499,"card":564,"direction":107,"ingested_from":108},"W7222974188",[546,549,552,555,558,561],{"name":547,"orcid":548},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":550,"orcid":551},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":553,"orcid":554},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":556,"orcid":557},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":559,"orcid":560},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":562,"orcid":563},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":565,"method":566,"finding":567,"direction":107,"opportunity":568},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z"]