[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2646":3,"related-2646":111},{"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":31,"secondary_topics":37,"classification":49,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":110},2646,"IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0356383","Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.","水稻叶片病害（RLD）的早期和实时检测对农民而言是一项重大挑战，尤其是在难以获取先进技术的农村地区。传统的深度学习模型通常需要大量计算资源，使其难以在实际农业环境中常用的移动或边缘设备上部署。尽管ResNet50、VGG16和InceptionV3等模型可以达到较高精度，但其计算成本高昂，可能不太适合在智慧农业系统中实时部署。此外，许多现有研究依赖有限的数据集，这可能限制模型在多样化真实条件下的泛化能力。尽管迁移学习可以提升分类性能，但开发适合实时物联网部署的轻量级模型仍具有挑战性。为解决这些局限，我们通过整合自采集样本和Kaggle样本，构建了一个包含7092张水稻叶片图像的混合数据集，并提出了IoT-RiceMobileNet，这是一种基于MobileNetV2改进的轻量级模型，可有效集成到我们开发的物联网系统中。所提出的模型优于迁移学习和深度学习基线模型，在保持低计算复杂度的同时，实现了99.19%的测试准确率和99.20%的精确率。我们进一步通过在构建的RLD数据集和多源数据集上采用分层5折和10折交叉验证确认了模型稳定性，在构建的RLD数据集上分别达到98.04%和98.13%的平均准确率，在多源数据集上分别达到98.11%和98.58%的平均准确率。此外，该模型紧凑的规模和85.17 FPS的推理速度支持实时部署。最后，该模型被集成到一个支持物联网的移动端、网页端和云端推理框架中，展示了其在智慧农业中可扩展水稻病害检测的实际潜力。",null,"PLoS ONE","2026-09-15T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"提出轻量级MobileNetV2水稻病害检测模型并集成物联网推理框架，实测精度与推理速度俱佳，方法新颖、数据规模可观，对边缘端智慧农业落地有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","物联网","水稻病害","轻量化模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,61,66,73],{"code":32,"score":56,"supported":57,"title":58,"abstract":59,"tags":60},15,true,[33],[34,33],[29],{"code":45,"score":56,"supported":57,"title":62,"abstract":64,"tags":65},[63],"IoT",[28,63],[28],{"code":39,"score":67,"supported":57,"title":68,"abstract":69,"tags":72},8,[],[70,71],"深度学习","deep learning",[27,30],{"code":74,"score":75,"supported":57,"title":76,"abstract":77,"tags":79},"I1",7,[],[26,78],"smart agriculture",[26],[81,82],"农业人工智能 轻量化模型 智慧农业 水稻病害","农业人工智能 轻量化模型","农业人工智能轻量化模型智慧农业水稻病害-2646",2,"10.1371\u002Fjournal.pone.0356383",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":102,"oa_url":6,"card":103,"direction":107,"ingested_from":109},"W7213328321",[89,91,93,95,97,99],{"name":90,"orcid":9},"Khawja Imran Masud",{"name":92,"orcid":9},"Md Yasin Zihad",{"name":94,"orcid":9},"Mehedi Hasan Shuvo",{"name":96,"orcid":9},"Mst Raonik Jannat",{"name":98,"orcid":9},"Jia Uddin",{"name":100,"orcid":101},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。","改进MobileNetV2，融合自采与Kaggle共7092张图像，IoT端部署","测试准确率99.19%，推理速度85.17 FPS，适合边缘设备实时检测。","智慧农业 \u002F 农业物联网","可探索多作物病害泛化、田间复杂光照下轻量模型鲁棒性及边缘联邦学习。","openalex","2026-09-16T23:30:12.197386Z",{"total":112,"page":21,"page_size":112,"items":113},6,[114,176,279,356,415,478],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":128,"tags":130,"primary_topic":132,"secondary_topics":133,"classification":136,"search_phrases":158,"slug":161,"view_count":21,"doi":162,"paper":163,"created_at":175},1892,"Deep learning technique for rice leaf disease classification and severity level identification via Hybrid ResConvolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-68755-5","Rice leaf diseases significantly reduce agricultural yield and pose a major challenge for sustainable food production, particularly owing to the limitations associated with manual and visual inspection methods that are subjective and often ineffective in early-stage detection. This investigation proposes a new Hybrid ResConvolutional Neural Network (HyResCN-Net) for effective classification and severity identification of rice plant leaf diseases. The proposed framework integrates an Internet of Things (IoT) -based data acquisition and routing simulation using CrowWhale Energy Trust Routing (CrowWhale-ETR) for efficient data handling. Initially, preprocessing is done by an averaging filter to reduce noise. Then, plant leaves are segmented using the Eff-UNet++ method. Augmentation techniques like rotation, scaling, and color change are applied to expand the dataset. Features, like entropy with Gradient Directional Pattern (GDP), Complete Local Binary Pattern (CLBP), and histogram features, are extracted to enhance feature representation. These features are then used within the proposed HyResCN-Net model, which integrates Parallel Convolutional Neural Network (PCNN) and ResNeXt to improve discriminative learning for disease classification and severity estimation. Experimental evaluation is conducted on the Rice Leaf Bacterial and Fungal Disease Dataset. Considering a k-value of 8, the HyResCN-Net gains an accuracy of 94.258%, a True Positive Rate (TPR) of 96.479%, a True Negative Rate (TNR) of 92.898%, a precision of 91.312% and an F1-score of 93.824% compared to existing methods. The HyResCN-Net efficiently enhances rice leaf disease identification and severity analysis, supporting its applicability in precision agriculture applications.","水稻叶部病害显著降低农业产量，并对可持续粮食生产构成重大挑战，尤其是由于人工目视检查方法存在主观性，且在早期检测中往往效果不佳。本研究提出了一种新型混合残差卷积神经网络（HyResConvolutional Neural Network，HyResCN-Net），用于水稻叶片病害的有效分类和严重程度识别。所提出的框架集成了基于物联网（Internet of Things，IoT）的数据采集和路由模拟，采用CrowWhale能量信任路由（CrowWhale-ETR）以实现高效的数据处理。首先，通过平均滤波器进行预处理以降低噪声。然后，利用Eff-UNet++方法对植物叶片进行分割。应用旋转、缩放和颜色变化等增强技术来扩充数据集。提取熵结合梯度方向模式（Gradient Directional Pattern，GDP）、完整局部二值模式（Complete Local Binary Pattern，CLBP）及直方图特征等特征，以增强特征表示。这些特征随后被用于所提出的HyResCN-Net模型中，该模型集成了并行卷积神经网络（Parallel Convolutional Neural Network，PCNN）和ResNeXt，以提升疾病分类和严重程度评估的判别学习能力。实验评估在水稻叶片细菌性和真菌性病害数据集上进行。在k值为8的情况下，与现有方法相比，HyResCN-Net达到了94.258%的准确率、96.479%的真阳性率（True Positive Rate，TPR）、92.898%的真阴性率（True Negative Rate，TNR）、91.312%的精确率以及93.824%的F1分数。HyResCN-Net有效增强了水稻叶片病害识别和严重程度分析，支持其在精准农业应用中的适用性。","Scientific Reports","2026-09-06T00:00:00Z",68,{"impact":56,"substance":124,"depth":17,"authority":125,"freshness":126,"relevant":21,"comment":127},20,12,3,"提出混合残差卷积网络用于水稻叶部病害分类与严重度识别，方法新颖且实验数据详实，对精准农业有参考价值。",[129],{"name":120,"url":117},[26,27,34,131],"病虫害防治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[134,135],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":137},{"candidates":138,"agricultural_evidence":57},[139,143,147,152],{"code":32,"score":56,"supported":57,"title":140,"abstract":141,"tags":142},[33],[34,33],[34],{"code":39,"score":20,"supported":57,"title":144,"abstract":145,"tags":146},[71],[],[27],{"code":45,"score":20,"supported":57,"title":148,"abstract":149,"tags":151},[],[28,150,63],"internet of things",[],{"code":74,"score":75,"supported":57,"title":153,"abstract":154,"tags":157},[],[155,156],"精准农业","precision agriculture",[26],[159,160],"农业人工智能 病虫害防治 智慧农业 水稻","农业人工智能 病虫害防治","农业人工智能病虫害防治智慧农业水稻-1892","10.1038\u002Fs41598-026-68755-5",{"doi":162,"openalex_id":164,"authors":165,"venue":120,"cited_by_count":102,"oa_url":117,"card":170,"direction":107,"ingested_from":109},"W7210271395",[166,168],{"name":167,"orcid":9},"R. Creesy",{"name":169,"orcid":9},"K. Michael Mahesh",{"tldr":171,"method":172,"finding":173,"direction":107,"opportunity":174},"提出混合Res卷积神经网络用于水稻叶部病害分类与严重度识别，结合物联网数据采集。","HyResCN-Net结合PCNN和ResNeXt，使用Eff-UNet++分割","在数据集上准确率达94.258%，优于现有方法，支持精准农业应用。","可探索模型在多种作物病害上的泛化能力，或结合边缘计算实现田间实时诊断。","2026-09-08T23:30:11.816325Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":184,"sources":186,"tags":188,"primary_topic":190,"secondary_topics":191,"classification":199,"search_phrases":254,"slug":257,"view_count":102,"doi":258,"paper":259,"created_at":278},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":185},"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。",[187],{"name":182,"url":179},[26,27,28,155,189],"气候韧性",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[192,193],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":194,"slug":195,"name":196,"path":197,"parent_code":198},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":50,"locked":14,"version":51,"method":52,"evidence":200},{"candidates":201,"agricultural_evidence":57},[202,211,216,222,228,235,242,248],{"code":39,"score":17,"supported":57,"title":203,"abstract":205,"tags":210},[204],"artificial intelligence",[70,206,207,208,71,209],"机器学习","计算机视觉","machine learning","computer vision",[27],{"code":74,"score":212,"supported":57,"title":213,"abstract":214,"tags":215},16,[156],[155,156],[26,155],{"code":45,"score":13,"supported":57,"title":217,"abstract":218,"tags":221},[],[28,219,63,220],"边缘计算","edge computing",[28],{"code":194,"score":20,"supported":57,"title":223,"abstract":224,"tags":227},[],[225,226,195],"灌溉","水资源",[],{"code":229,"score":75,"supported":57,"title":230,"abstract":231,"tags":234},"H7",[],[232,233],"气候变化","climate change",[189],{"code":236,"score":112,"supported":57,"title":237,"abstract":238,"tags":241},"H2",[],[239,240],"土壤","soil",[],{"code":243,"score":126,"supported":57,"title":244,"abstract":245,"tags":247},"A7",[],[246],"间作",[],{"code":249,"score":126,"supported":57,"title":250,"abstract":251,"tags":253},"F3",[],[252],"养分管理",[],[255,256],"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":258,"openalex_id":260,"authors":261,"venue":182,"cited_by_count":102,"oa_url":179,"card":272,"direction":107,"ingested_from":109},"W7223121456",[262,265,267,269],{"name":263,"orcid":264},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":266,"orcid":9},"Oluwadare OYEBODE",{"name":268,"orcid":9},"Iretiayo Adelaiye",{"name":270,"orcid":271},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":273,"method":274,"finding":275,"direction":276,"opportunity":277},"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。","农业人工智能与决策模型","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":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":291,"tags":293,"primary_topic":295,"secondary_topics":300,"classification":303,"search_phrases":337,"slug":340,"view_count":21,"doi":341,"paper":342,"created_at":355},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":212,"substance":124,"depth":289,"authority":125,"freshness":20,"relevant":21,"comment":290},17,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[292],{"name":285,"url":282},[26,27,28,155,294],"棉花病害",{"code":296,"slug":297,"name":298,"path":299,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops",[301,302],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":304},{"candidates":305,"agricultural_evidence":57},[306,310,317,322,326,333],{"code":45,"score":56,"supported":57,"title":307,"abstract":308,"tags":309},[63],[28,63],[28],{"code":296,"score":311,"supported":57,"title":312,"abstract":314,"tags":316},14,[313],"cotton",[315,313],"棉花",[],{"code":74,"score":318,"supported":57,"title":319,"abstract":320,"tags":321},11,[],[26,155,156],[26,155],{"code":39,"score":13,"supported":57,"title":323,"abstract":324,"tags":325},[],[70,207,71,204,209],[27],{"code":327,"score":112,"supported":57,"title":328,"abstract":329,"tags":332},"F4",[],[330,331],"农药","pesticide",[],{"code":236,"score":112,"supported":57,"title":334,"abstract":335,"tags":336},[],[239,240],[],[338,339],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":341,"openalex_id":343,"authors":344,"venue":285,"cited_by_count":102,"oa_url":282,"card":350,"direction":107,"ingested_from":109},"W7221340722",[345,347],{"name":346,"orcid":9},"Deepak R Khadse",{"name":348,"orcid":349},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":351,"method":352,"finding":353,"direction":107,"opportunity":354},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":371,"secondary_topics":372,"classification":374,"search_phrases":401,"slug":404,"view_count":102,"doi":405,"paper":406,"created_at":414},4658,"Energy-Efficient Smart Agriculture Management Framework Using IoT and Ensemble Machine Learning for Intelligent Crop Health Monitoring and Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.62s.32","Context: Smart agriculture plays an important role in addressing the challenges of food security, environmental sustainability, and sustainable farming through IoT.Farmers are facing many issues in traditional farming, such as inefficient monitoring and poor crop health detection.Traditional farming has a negative impact on crop production and sustainability.In this regard, the development of intelligent and energy-efficient agricultural monitoring systems has become crucial for contemporary precision farming applications.Objective: This study aims to monitor crop health and optimize resource use using machine learning techniques.Our proposed system intends to monitor crop conditions in real time, enhance agricultural management, and minimize unnecessary sensor utilization and communication overhead.Material\u002Fmethods: In this paper, we used five machine learning classifiers to monitor crop health using sensor data.The agricultural sensor data (soil moisture, soil temperature, soil pH, humidity, air temperature, solar radiation, wind speed, NDVI index, and 5G latency) were used for crop health monitoring.The models, such as RF, XGBoost, LightGBM, and CatBoost, and soft voting were used for crop health classification into the Healthy, High_Stress, and Moderate_Stress categories.The experimental observation revealed that soft voting achieved the highest accuracy of 94.5%, macro-F1 score of 0.945, weighted F1 score of 0.945, and AUC-ROC score of 0.988 compared to the other models.The cross-validation results further validated the robustness and stability of the proposed framework.The IoTenabled intelligent agriculture management system offers an efficient, scalable solution for real-time crop health monitoring, energy-efficient resource optimization, and sustainable smart farming.","背景：智慧农业在借助物联网（IoT）应对粮食安全、环境可持续性和可持续耕作等挑战方面发挥着重要作用。农民在传统耕作中面临诸多问题，例如监测效率低下和作物健康检测不佳。传统耕作对作物产量和可持续性产生负面影响。在此背景下，开发智能且节能的农业监测系统对于当代精准农业应用已变得至关重要。目标：本研究旨在利用机器学习技术监测作物健康并优化资源利用。我们提出的系统旨在实时监测作物状况、增强农业管理并最大限度减少不必要的传感器使用和通信开销。材料\u002F方法：在本文中，我们使用了五种机器学习分类器，利用传感器数据监测作物健康。农业传感器数据（土壤湿度、土壤温度、土壤pH值、湿度、空气温度、太阳辐射、风速、NDVI指数和5G延迟）被用于作物健康监测。RF、XGBoost、LightGBM和CatBoost等模型以及软投票被用于将作物健康分类为健康、高胁迫和中等胁迫类别。实验观察表明，与其他模型相比，软投票达到了最高的准确率94.5%、宏F1分数0.945、加权F1分数0.945和AUC-ROC分数0.988。交叉验证结果进一步验证了所提出框架的鲁棒性和稳定性。该物联网赋能的智能农业管理系统为实时作物健康监测、节能资源优化和可持续智慧农业提供了一种高效、可扩展的解决方案。","International Journal of Drug Delivery Technology",52,{"impact":67,"substance":212,"depth":311,"authority":112,"freshness":67,"relevant":21,"comment":365},"方法组合与多源传感器数据有一定新意，但期刊权威性弱、属实验室验证，公共价值有限，不宜进入每日精选。",[367],{"name":362,"url":359},[26,27,28,369,370],"作物健康监测","资源优化",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},[373],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":375},{"candidates":376,"agricultural_evidence":57},[377,381,386,390,397],{"code":74,"score":17,"supported":57,"title":378,"abstract":379,"tags":380},[78],[26,155,78],[26],{"code":45,"score":17,"supported":57,"title":382,"abstract":383,"tags":385},[63],[28,384,63],"农业传感",[28],{"code":39,"score":56,"supported":57,"title":387,"abstract":388,"tags":389},[208],[206,208],[27],{"code":391,"score":112,"supported":57,"title":392,"abstract":393,"tags":396},"A8",[],[394,395],"粮食安全","food security",[],{"code":236,"score":112,"supported":57,"title":398,"abstract":399,"tags":400},[],[239,240],[],[402,403],"IoT 作物健康监测","集成学习 精准农业","IoT作物健康监测-4658","10.25258\u002Fijddt.16.62s.32",{"doi":405,"openalex_id":407,"authors":408,"venue":362,"cited_by_count":102,"oa_url":359,"card":409,"direction":107,"ingested_from":109},"W7221334384",[],{"tldr":410,"method":411,"finding":412,"direction":107,"opportunity":413},"用IoT传感器数据与集成机器学习实现作物健康分类与资源优化。","五种分类器（RF、XGBoost、LightGBM、CatBoost、软投票）结","软投票集成模型准确率达94.5%，AUC-ROC为0.988，优于单一模型。","可探索边缘端轻量化集成模型与能耗-精度联合优化，降低传感器通信开销。","2026-10-11T23:30:07.636015Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":433,"search_phrases":457,"slug":460,"view_count":21,"doi":461,"paper":462,"created_at":477},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":67,"substance":17,"depth":56,"authority":112,"freshness":67,"relevant":21,"comment":425},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[427],{"name":421,"url":418},[26,27,219,28,429],"精准灌溉",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},[432],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":434},{"candidates":435,"agricultural_evidence":57},[436,440,445,449,453],{"code":45,"score":318,"supported":57,"title":437,"abstract":438,"tags":439},[],[28,150,63],[28,219],{"code":39,"score":13,"supported":57,"title":441,"abstract":442,"tags":444},[],[70,207,71,209,443],"decision support",[27],{"code":194,"score":75,"supported":57,"title":446,"abstract":447,"tags":448},[],[225,195],[429],{"code":74,"score":75,"supported":57,"title":450,"abstract":451,"tags":452},[],[155,156],[26],{"code":236,"score":112,"supported":57,"title":454,"abstract":455,"tags":456},[],[239,240],[],[458,459],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":461,"openalex_id":463,"authors":464,"venue":421,"cited_by_count":102,"oa_url":418,"card":472,"direction":107,"ingested_from":109},"W7220855887",[465,468,470],{"name":466,"orcid":467},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":469,"orcid":9},"Shetty Chirag",{"name":471,"orcid":9},"Sunay Kanade",{"tldr":473,"method":474,"finding":475,"direction":107,"opportunity":476},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":9,"source_name":484,"source_url":481,"published_at":485,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":486,"score_detail":487,"sources":489,"tags":491,"primary_topic":494,"secondary_topics":495,"classification":501,"search_phrases":542,"slug":545,"view_count":84,"doi":546,"paper":547,"created_at":560},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":311,"freshness":20,"relevant":21,"comment":488},"提出农业数字孪生与GRU融合的实时产量预测与决策支持框架，方法新颖、指标扎实，对智慧农业研究有参考价值。",[490],{"name":484,"url":481},[26,27,28,492,493],"数字孪生","作物产量预测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[496,497],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":229,"slug":498,"name":499,"path":500,"parent_code":198},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":50,"locked":14,"version":51,"method":52,"evidence":502},{"candidates":503,"agricultural_evidence":57},[504,510,514,518,523,527,531,537],{"code":74,"score":505,"supported":57,"title":506,"abstract":508,"tags":509},19,[507],"digital twin",[26,492,78,507],[492,26],{"code":39,"score":125,"supported":57,"title":511,"abstract":512,"tags":513},[443],[204],[27],{"code":45,"score":13,"supported":57,"title":515,"abstract":516,"tags":517},[],[28,150,63],[28],{"code":229,"score":20,"supported":57,"title":519,"abstract":520,"tags":522},[],[232,521,233],"气候适应",[],{"code":236,"score":112,"supported":57,"title":524,"abstract":525,"tags":526},[],[239,240],[],{"code":194,"score":112,"supported":57,"title":528,"abstract":529,"tags":530},[],[225,195],[],{"code":532,"score":126,"supported":57,"title":533,"abstract":534,"tags":536},"H8",[],[535],"可持续农业",[],{"code":538,"score":21,"supported":14,"title":539,"abstract":540,"tags":541},"I3",[],[],[493],[543,544],"Agri Digital Twin GRU 产量预测","物联网 数字孪生 智慧农业","AgriDigitalTwinGRU产量预测-4213","10.1016\u002Fj.engappai.2026.116418",{"doi":546,"openalex_id":548,"authors":549,"venue":484,"cited_by_count":102,"oa_url":481,"card":555,"direction":107,"ingested_from":109},"W7219849006",[550,553],{"name":551,"orcid":552},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":554,"orcid":9},"Munish Bhatia",{"tldr":556,"method":557,"finding":558,"direction":107,"opportunity":559},"提出农业数字孪生框架，结合GRU与物联网实现实时产量预测与可持续决策支持。","基于物联网传感实时采集土壤气象数据，用GRU模型做时序产量预测与分类。","预测MAE 0.0243、R² 0.9254，平均准确率97.41%，优于BiLSTM、LSTM和1","可探索多源遥感与数字孪生融合、跨区域迁移学习及决策闭环验证等空白。","2026-10-05T23:30:09.119891Z"]