[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2963":3,"related-2963":93},{"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":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":92},2963,"A comprehensive review of deep learning methods for weed classification in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-01916-7","Weeds today are among the factors contributing to low agricultural productivity. As the world’s population continues to grow, there is an urgent need to meet global food demand. Nigeria currently lacks sufficient crop production to feed its growing population, and weeds are among the core contributors to poor agricultural yield. This study conducts a comprehensive review of Deep Learning (DL) approaches to weed classification in precision agriculture, covering literature from 2018 to 2025, was carried out. We employed a mix of quantitative and qualitative methods in the course of this review paper. Our data source is centred on Scopus-indexed papers, published with Sensors, Electronics, and Agriculture in MDPI as well as IEEE, Thomson Reuters, and Springer. The study systematically reviewed and analysed machine learning (ML), DL, and instance segmentation techniques to identify the key technological and environmental barriers, such as data limitations, class imbalance, environmental variability, and model scalability issues that affect the effectiveness and efficiency of these models when deployed in real time. These findings show that while weed management models like the YOLO variants, ResNet, and Vision Transformers achieved high accuracy in training and testing, they are associated with several challenges in their real world-deployment, such as occlusion, small object detection, and environmental adaptability. Overall, this research provides recommended solutions to enhance model robustness, scalability, and efficiency. It further provides a summary of the current state and future directions for AI-driven weed management.","杂草是当前导致农业生产力低下的因素之一。随着世界人口持续增长，满足全球粮食需求已成为迫切任务。尼日利亚目前的作物产量不足以养活其不断增长的人口，而杂草是导致农业产量低下的核心因素之一。本研究对精准农业中基于深度学习（Deep Learning，DL）的杂草分类方法进行了全面综述，涵盖2018年至2025年的文献。在综述过程中，我们采用了定量与定性相结合的方法。数据来源集中于Scopus索引论文，这些论文发表于MDPI旗下的Sensors、Electronics和Agriculture，以及IEEE、Thomson Reuters和Springer。本研究系统综述并分析了机器学习（Machine Learning，ML）、深度学习及实例分割技术，以识别影响这些模型实时部署效果与效率的关键技术和环境障碍，如数据局限性、类别不平衡、环境变异性及模型可扩展性问题。研究结果表明，尽管YOLO系列、ResNet和视觉Transformer（Vision Transformer）等杂草管理模型在训练和测试中达到了较高精度，但在实际部署中仍面临诸多挑战，如遮挡、小目标检测和环境适应性等问题。总体而言，本研究提出了增强模型鲁棒性、可扩展性和效率的推荐解决方案，并进一步总结了人工智能驱动杂草管理的现状与未来方向。",null,"Discover Artificial Intelligence","2026-09-18T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,13,8,1,"系统综述2018—2025年深度学习杂草分类方法，指出遮挡、小目标与环境适应性等落地瓶颈，对农业AI研究有参考价值，但属综述类论文、非突破性成果。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","深度学习","杂草识别","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,56,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":55},19,true,[49],"deep learning",[51,29,52,53,49,54],"目标检测","机器学习","machine learning","YOLO",[28,29],{"code":57,"score":19,"supported":47,"title":58,"abstract":60,"tags":61},"I1",[59],"precision agriculture",[31,59],[27,31],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"F4",[],[],[30],[68,69],"深度学习 杂草分类 精准农业","YOLO 杂草识别 模型部署","深度学习杂草分类精准农业-2963",3,"10.1007\u002Fs44163-026-01916-7",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":89,"ingested_from":91},"W7213558223",[76,78,80,82],{"name":77,"orcid":9},"Njoku Camillus Ekene",{"name":79,"orcid":9},"Francis A. Okoye",{"name":81,"orcid":9},"Ebere Uzoka Chidi",{"name":83,"orcid":9},"OGBU MARY NNENNA",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。","混合定量定性法，基于Scopus及MDPI、IEEE等文献，分析ML、DL与实例","YOLO、ResNet、ViT等精度高，但实际部署受遮挡、小目标与环境适应性限制。","农业人工智能与决策模型","可研究轻量化、跨域自适应模型，解决小目标与遮挡下的实时杂草识别难题。","openalex","2026-09-19T23:30:56.875661Z",{"total":94,"page":22,"page_size":94,"items":95},6,[96,157,217,279,355,413],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":110,"tags":112,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":137,"slug":140,"view_count":71,"doi":141,"paper":142,"created_at":156},2335,"Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10343-026-01424-9","Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review。Journal of Plant Diseases and Protection","基于深度学习的精准农业杂草分类、检测与分割：文献计量与综合综述。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-12T00:00:00Z",75,{"impact":106,"substance":107,"depth":18,"authority":108,"freshness":21,"relevant":22,"comment":109},15,20,14,"核心期刊发表的深度学习杂草识别综述，方法梳理与文献计量兼具，对精准农业植保智能化有参考价值，但属学术综述而非产业级突破。",[111],{"name":102,"url":99},[27,28,29,30,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":116},{"candidates":117,"agricultural_evidence":47},[118,122,126,133],{"code":57,"score":19,"supported":47,"title":119,"abstract":120,"tags":121},[59],[31,59],[27,31],{"code":33,"score":19,"supported":47,"title":123,"abstract":124,"tags":125},[49],[29,49],[28,29],{"code":127,"score":108,"supported":47,"title":128,"abstract":130,"tags":132},"I7",[129],"bibliometric",[131,129],"文献计量",[],{"code":63,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[30],[138,139],"农业人工智能 智慧农业 杂草识别 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业杂草识别深度学习-2335","10.1007\u002Fs10343-026-01424-9",{"doi":141,"openalex_id":143,"authors":144,"venue":102,"cited_by_count":84,"oa_url":9,"card":151,"direction":89,"ingested_from":91},"W7212352052",[145,148],{"name":146,"orcid":147},"Furkan Ulaş","https:\u002F\u002Forcid.org\u002F0009-0002-3052-4457",{"name":149,"orcid":150},"Muhammad Aasım","https:\u002F\u002Forcid.org\u002F0000-0002-8524-9029",{"tldr":152,"method":153,"finding":154,"direction":89,"opportunity":155},"对精准农业中深度学习杂草分类、检测与分割研究进行文献计量与综述分析。","文献计量分析与综合综述，基于相关文献数据库。","梳理了深度学习在杂草识别三大任务中的方法、趋势与挑战。","可针对田间复杂光照与多物种场景，探索轻量化实时分割模型与跨数据集泛化。","2026-09-13T23:30:38.907624Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":165,"sources":168,"tags":170,"primary_topic":172,"secondary_topics":173,"classification":174,"search_phrases":199,"slug":202,"view_count":203,"doi":204,"paper":205,"created_at":216},4912,"Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231","Plant diseases pose a significant threat to global food security, crop yield, and agricultural sustainability. Traditional disease diagnosis methods, relying on expert visual inspection and laboratory analyses, are often time-consuming, labor-intensive, and costly, limiting their scalability. Recent advances in artificial intelligence, particularly deep learning, offer automated and accurate solutions for plant disease detection. This review synthesizes current research on deep learning–based approaches, focusing on convolutional neural networks (CNNs), hybrid CNN-transformer models, attention mechanisms, and generative data augmentation techniques. CNNs have demonstrated strong capabilities in extracting hierarchical features from leaf images, capturing complex patterns such as lesion shapes, color variations, and textures, thereby outperforming conventional machine learning and rule-based methods. Hybrid architectures and transformer-based models enhance feature extraction, particularly for subtle or visually similar disease symptoms. Data augmentation strategies, including generative adversarial networks (GANs), help address class imbalance and improve model generalization. Despite high reported accuracies, challenges remain, including limited dataset diversity, environmental variability, overfitting, lack of explainable AI, and computational constraints for mobile deployment. Future research should prioritize explainable models, multi-crop and multi-disease detection, lightweight architectures suitable for field applications, and integration with precision agriculture systems. Overall, deep learning–based plant disease detection presents a transformative approach for early diagnosis, timely intervention, and sustainable agricultural practices, offering the potential to reduce crop losses, enhance productivity, and contribute to global food security.","植物病害对全球粮食安全、作物产量和农业可持续性构成重大威胁。传统的病害诊断方法依赖专家视觉检查和实验室分析，往往耗时、费力且成本高昂，限制了其可扩展性。人工智能，特别是深度学习的最新进展，为植物病害检测提供了自动化且准确的解决方案。本文综述了基于深度学习方法的最新研究，重点关注卷积神经网络（CNN）、CNN-Transformer混合模型、注意力机制和生成式数据增强技术。CNN在从叶片图像中提取层次化特征方面展现出强大能力，能够捕捉病变形状、颜色变化和纹理等复杂模式，从而优于传统机器学习和基于规则的方法。混合架构和基于Transformer的模型增强了特征提取能力，尤其针对细微或视觉上相似的病害症状。数据增强策略，包括生成对抗网络（GAN），有助于解决类别不平衡问题并提高模型泛化能力。尽管已有研究报告了较高的准确率，但仍存在诸多挑战，包括数据集多样性有限、环境变异性、过拟合、缺乏可解释的人工智能以及移动端部署的计算约束。未来研究应优先关注可解释模型、多作物和多病害检测、适用于田间应用的轻量级架构以及与精准农业系统的集成。总体而言，基于深度学习的植物病害检测为早期诊断、及时干预和可持续农业实践提供了一种变革性方法，具有减少作物损失、提高生产力并促进全球粮食安全的潜力。","INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-12T00:00:00Z",{"impact":18,"substance":107,"depth":166,"authority":17,"freshness":21,"relevant":22,"comment":167},17,"系统综述深度学习植物病害检测的方法进展与挑战，学术价值较高但偏理论，对产业落地有参考意义。",[169],{"name":163,"url":160},[27,28,29,31,171],"植物病害检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":175},{"candidates":176,"agricultural_evidence":47},[177,182,186,193],{"code":33,"score":46,"supported":47,"title":178,"abstract":179,"tags":181},[49],[29,52,53,49,180],"artificial intelligence",[28,29],{"code":57,"score":21,"supported":47,"title":183,"abstract":184,"tags":185},[],[31,59],[27,31],{"code":187,"score":94,"supported":47,"title":188,"abstract":189,"tags":192},"A8",[],[190,191],"粮食安全","food security",[],{"code":194,"score":71,"supported":47,"title":195,"abstract":196,"tags":198},"H8",[],[197],"可持续农业",[],[200,201],"植物病害检测 深度学习","卷积神经网络 作物病害识别","植物病害检测深度学习-4912",2,"10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231",{"doi":204,"openalex_id":206,"authors":207,"venue":163,"cited_by_count":84,"oa_url":210,"card":211,"direction":89,"ingested_from":91},"W7221637766",[208],{"name":209,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 12 2025\u002FDeep Learning Approaches for Plant 220-231.pdf",{"tldr":212,"method":213,"finding":214,"direction":89,"opportunity":215},"综述深度学习在植物病害检测中的应用，涵盖CNN、混合Transformer、注意力机制与生成式数据增","综述CNN、CNN-Transformer混合模型、注意力机制及GAN数据增强在","深度学习检测精度高，但仍受数据集多样性、环境变异、可解释性和移动部署算力限制。","可探索轻量化可解释模型、多作物多病害检测及与精准农业系统集成，解决田间部署难题。","2026-10-14T23:30:54.687367Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":225,"is_selected":14,"score":15,"score_detail":226,"sources":229,"tags":233,"primary_topic":235,"secondary_topics":241,"classification":243,"search_phrases":260,"slug":263,"view_count":22,"doi":264,"paper":265,"created_at":278},4241,"Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142591","Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.","植物病害对农业产生重大的社会、生态和经济影响，并经常给农民带来巨大的经济损失。因此，早期且精确的病害检测对于高效的作物管理至关重要。近期研究强调了叶部病害的严重程度及其对产量品质的直接影响。数码相机和平板扫描仪均可用于拍摄叶片图像，但它们各有一定缺陷。尽管扫描仪提供了更安全、更可控的图像获取方式，但其速度相对较慢，且可能产生边缘阴影。本研究采用混合深度学习方法识别苹果植株叶部病害。为提高分类性能，所提方法结合了残差网络（ResNet）、遗传算法（GA）和卷积神经网络（CNN）的优势。CNN用于提取特征并分类病害，而ResNet增强了更深网络的学习效率。利用遗传算法对特征集进行优化，从而提高准确率并降低计算复杂度。该模型还计算了受影响的叶面积百分比。实验结果表明，所提方法以高准确率和低计算成本检测叶部病害，使其成为可靠且有效的精准农业解决方案。所提模型在多种叶片条件下均表现出较强的分类效率，健康叶片准确率为0.97，苹果黑星病为0.994，锈病为0.89，白粉病为0.91。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-04T00:00:00Z",25,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":227,"relevant":22,"comment":228},9,"基于ResNet+GA+CNN的苹果叶病识别研究，准确率数据具体，方法有增量，但属实验室论文，产业影响有限。",[230,231],{"name":223,"url":220},{"name":223,"url":232},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142592",[27,28,29,31,234],"苹果病害",{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[242],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":244},{"candidates":245,"agricultural_evidence":47},[246,252,256],{"code":236,"score":106,"supported":47,"title":247,"abstract":249,"tags":251},[248],"apple",[250,248],"苹果",[234],{"code":57,"score":21,"supported":47,"title":253,"abstract":254,"tags":255},[],[31,59],[27,31],{"code":33,"score":21,"supported":47,"title":257,"abstract":258,"tags":259},[],[29,49],[28,29],[261,262],"苹果叶片病害 卷积神经网络","苹果锈病 白粉病 识别","苹果叶片病害卷积神经网络-4241","10.5281\u002Fzenodo.23142591",{"doi":264,"openalex_id":266,"authors":267,"venue":223,"cited_by_count":84,"oa_url":220,"card":272,"direction":277,"ingested_from":91},"W7219637192",[268,270],{"name":269,"orcid":9},"Mohd Arshad Raza",{"name":271,"orcid":9},"Kiran Pandey",{"tldr":273,"method":274,"finding":275,"direction":89,"opportunity":276},"提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。","ResNet提取深层特征，遗传算法优化特征集，CNN分类，苹果叶片图像数据。","健康叶、黑星病、锈病、白粉病准确率分别达0.97、0.994、0.89、0.91，计算成本低。","可探索轻量化模型在田间移动端实时检测，并融合多病害严重度分级与产量损失预测。","数字乡村与农业信息化","2026-10-05T23:30:30.830366Z",{"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":290,"tags":292,"primary_topic":294,"secondary_topics":300,"classification":302,"search_phrases":337,"slug":340,"view_count":341,"doi":342,"paper":343,"created_at":354},2940,"A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection","https:\u002F\u002Fdoi.org\u002F10.53365\u002Fnrfhh.1816","Cotton production is frequently affected by leaf diseases that can reduce plant productivity, deteriorate crop quality, and cause considerable financial losses for farmers. Consequently, rapid and reliable disease identification is an important requirement for precision agriculture and effective crop protection. Conventional image-based approaches predominantly employ CNN architectures for visual classification; however, such methods may have limited capability in learning long-range spatial dependencies and modeling changes associated with disease development over time. To overcome these limitations, this research introduces an integrated hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Transformer-based self-attention, and Long Short-Term Memory (LSTM) networks for intelligent cotton leaf disease recognition. The proposed framework utilizes a pre-trained EfficientNet network to extract discriminative spatial characteristics from cotton leaf images. The extracted representations are subsequently processed through a multi-head self-attention Transformer module, which enables the network to identify relationships between distant and relevant regions of the leaf. An LSTM component is then incorporated to learn sequential dependencies and provide a foundation for analyzing disease evolution and progression. To improve model reliability and generalization, the network is trained using an appropriately organized cotton leaf image dataset together with image augmentation, transfer learning, and fine-tuning techniques. The experimental evaluation indicates that the proposed CNN–Transformer–LSTM architecture provides improved disease classification performance and more effective feature learning compared with conventional CNN-based models. Performance assessment using classification metrics, confusion matrices, and ROC curves demonstrates strong discrimination among the considered cotton disease categories. The combined learning of local visual characteristics, global contextual information, and sequential dependencies provides a comprehensive framework for automated cotton disease assessment. The proposed approach can serve as a scalable artificial intelligence solution for precision agriculture applications. Its architecture also provides opportunities for future integration with IoT-based crop monitoring systems, environmental data acquisition, and disease progression prediction, supporting early warning systems and intelligent crop health management in smart farming environments.","棉花生产常受叶片病害影响，这些病害会降低植株生产力、恶化作物品质，并给农民造成相当大的经济损失。因此，快速可靠的病害识别是精准农业和有效作物保护的重要需求。传统的基于图像的方法主要采用CNN架构进行视觉分类；然而，此类方法在学习长程空间依赖关系以及建模与病害随时间发展相关的变化方面能力有限。为克服这些局限，本研究提出了一种集成混合深度学习框架，将卷积神经网络（CNN）、基于Transformer的自注意力机制和长短期记忆（LSTM）网络相结合，用于智能棉花叶片病害识别。所提出的框架利用预训练的EfficientNet网络从棉花叶片图像中提取具有判别力的空间特征。提取到的表示随后通过多头自注意力Transformer模块进行处理，使网络能够识别叶片中相距较远且相关区域之间的关系。随后引入LSTM组件以学习序列依赖关系，并为分析病害演变和进展提供基础。为提高模型可靠性和泛化能力，网络使用组织良好的棉花叶片图像数据集进行训练，并结合图像增强、迁移学习和微调技术。实验评估表明，与传统的基于CNN的模型相比，所提出的CNN–Transformer–LSTM架构提供了更好的病害分类性能和更有效的特征学习。使用分类指标、混淆矩阵和ROC曲线进行的性能评估表明，该方法在所考虑的棉花病害类别之间具有较强的判别能力。局部视觉特征、全局上下文信息和序列依赖关系的联合学习为自动化棉花病害评估提供了一个全面的框架。所提出的方法可作为精准农业应用中可扩展的人工智能解决方案。其架构也为未来与基于物联网的作物监测系统、环境数据采集和病害进展预测的集成提供了机会，从而支持预警系统和智能","Natural Resources for Human Health","2026-09-16T00:00:00Z",69,{"impact":17,"substance":107,"depth":166,"authority":17,"freshness":21,"relevant":22,"comment":289},"提出CNN-Transformer-LSTM混合框架用于棉花叶病识别，方法有创新但属实验室验证阶段，产业影响有限。",[291],{"name":285,"url":282},[27,28,29,31,293],"棉花病害识别",{"code":295,"slug":296,"name":297,"path":298,"parent_code":299},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[301],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":303},{"candidates":304,"agricultural_evidence":47},[305,309,315,319,326,332],{"code":33,"score":46,"supported":47,"title":306,"abstract":307,"tags":308},[49],[29,49,180],[28,29],{"code":295,"score":108,"supported":47,"title":310,"abstract":312,"tags":314},[311],"cotton",[313,311],"棉花",[],{"code":57,"score":21,"supported":47,"title":316,"abstract":317,"tags":318},[],[31,59],[27,31],{"code":320,"score":94,"supported":47,"title":321,"abstract":322,"tags":325},"I5",[],[323,324],"物联网","IoT",[],{"code":327,"score":71,"supported":47,"title":328,"abstract":329,"tags":331},"A4",[],[330],"花生",[],{"code":63,"score":71,"supported":47,"title":333,"abstract":334,"tags":336},[],[335],"crop protection",[],[338,339],"棉花叶病 CNN Transformer LSTM","EfficientNet 棉花病害检测","棉花叶病CNNTransformerLSTM-2940",5,"10.53365\u002Fnrfhh.1816",{"doi":342,"openalex_id":344,"authors":345,"venue":285,"cited_by_count":84,"oa_url":9,"card":348,"direction":353,"ingested_from":91},"W7213544712",[346],{"name":347,"orcid":9},"Prajakta Sunil Gupta",{"tldr":349,"method":350,"finding":351,"direction":89,"opportunity":352},"提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。","EfficientNet提取特征，Transformer自注意力与LSTM建模，","混合模型分类性能优于传统CNN，能同时学习局部特征、全局上下文和序列依赖。","可融合IoT环境数据与时间序列，开展病害进展预测和早期预警系统研究。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:19.558449Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":10,"source_url":358,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":362,"score_detail":363,"sources":365,"tags":367,"primary_topic":368,"secondary_topics":369,"classification":370,"search_phrases":393,"slug":395,"view_count":71,"doi":396,"paper":397,"created_at":412},2179,"Comparative evaluation of classical machine learning and deep learning models for early weed detection in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02195-y","Unless treated and controlled early, weeds considerably impede the growth of crops as they compete over vital factors (resources) like nutrients, water, and light. To overcome this problem, an automated weed species early detection and classification system using machine learning-based image analysis is suggested. The framework integrates classical machine learning algorithms Support Vector Machines (SVM), Random Forests and k-Nearest Neighbors (k-NN), using handcrafted features like texture, shape, and color with deep learning models, Convolutional Neural Networks (CNNs) which automatically learn discriminative features in the data. The study comparatively evaluates classical ML and deep learning models. In order to test the experiments, a publicly available Sugar Beet dataset was used, as well as a custom maize seedling dataset. To enhance generalization and robustness, some preprocessing steps were performed, including normalization, background removal, and augmentation. Common metrics used to assess the performance of the models are F1-score and Intersection over Union (IoU). The experimental results show deep learning models outperforming the traditional machine learning methods, especially CNNs The CNN model also exhibited a classification accuracy of 98.7%. These results demonstrate the promise of deep learning to quickly, reliably, and in large scale detect weeds in the early stages of precision agriculture. This study highlights the feasibility of machine learning systems in proactive management of weed as well as in supporting healthy crop growth at the early stages of development.","除非在早期进行处理和控制，否则杂草会争夺养分、水分和光照等关键因素（资源），从而严重阻碍作物生长。为解决这一问题，提出了一种基于机器学习的图像分析自动化杂草物种早期检测与分类系统。该框架整合了经典机器学习算法——支持向量机（SVM）、随机森林和k近邻（k-NN），利用纹理、形状和颜色等手工特征，并结合深度学习模型——卷积神经网络（CNN），后者可自动学习数据中的判别性特征。本研究对经典机器学习和深度学习模型进行了对比评估。为验证实验，使用了公开的甜菜数据集以及自建的玉米幼苗数据集。为增强泛化性和鲁棒性，执行了一些预处理步骤，包括归一化、背景去除和数据增强。用于评估模型性能的常用指标为F1分数和交并比（IoU）。实验结果表明，深度学习模型优于传统机器学习方法，尤其是CNN。CNN模型的分类准确率达到了98.7%。这些结果证明了深度学习在精准农业中快速、可靠且大规模地早期检测杂草的潜力。本研究凸显了机器学习系统在杂草主动管理以及支持作物早期健康生长方面的可行性。","2026-09-10T00:00:00Z",72,{"impact":19,"substance":107,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":364},"对比经典机器学习与深度学习在作物早期杂草检测中的表现，CNN 分类准确率达 98.7%，方法扎实、结论明确，对精准农业智能除草有参考价值。",[366],{"name":10,"url":358},[27,28,52,30,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":371},{"candidates":372,"agricultural_evidence":47},[373,378,382,389],{"code":33,"score":374,"supported":47,"title":375,"abstract":376,"tags":377},27,[53,49],[29,52,53,49],[28,52],{"code":57,"score":19,"supported":47,"title":379,"abstract":380,"tags":381},[59],[31,59],[27,31],{"code":383,"score":94,"supported":47,"title":384,"abstract":385,"tags":388},"A3",[],[386,387],"玉米","maize",[],{"code":63,"score":22,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[30],[394,139],"农业人工智能 智慧农业 机器学习 杂草识别","农业人工智能智慧农业机器学习杂草识别-2179","10.1007\u002Fs44163-026-02195-y",{"doi":396,"openalex_id":398,"authors":399,"venue":10,"cited_by_count":84,"oa_url":358,"card":407,"direction":89,"ingested_from":91},"W7212111551",[400,403,405],{"name":401,"orcid":402},"Rajeev Kumar","https:\u002F\u002Forcid.org\u002F0000-0001-8414-3778",{"name":404,"orcid":9},"P. K. Singh",{"name":406,"orcid":9},"Rohit Kumar Tiwari",{"tldr":408,"method":409,"finding":410,"direction":89,"opportunity":411},"对比经典机器学习与深度学习模型在精准农业早期杂草检测中的性能。","用SVM、随机森林、k-NN及CNN，基于甜菜和玉米幼苗图像数据集。","CNN分类准确率达98.7%，显著优于传统机器学习方法。","可探索轻量化模型在田间边缘设备实时检测杂草的部署与跨物种泛化能力。","2026-09-11T23:30:52.642134Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":419,"source_url":416,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":425,"tags":427,"primary_topic":429,"secondary_topics":430,"classification":441,"search_phrases":493,"slug":496,"view_count":84,"doi":497,"paper":498,"created_at":516},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":18,"substance":423,"depth":18,"authority":20,"freshness":227,"relevant":22,"comment":424},22,"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。",[426],{"name":419,"url":416},[27,28,323,31,428],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[431,435],{"code":320,"slug":432,"name":433,"path":434,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":436,"slug":437,"name":438,"path":439,"parent_code":440},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":442},{"candidates":443,"agricultural_evidence":47},[444,450,454,460,466,474,481,487],{"code":33,"score":18,"supported":47,"title":445,"abstract":446,"tags":449},[180],[29,52,447,53,49,448],"计算机视觉","computer vision",[28],{"code":57,"score":19,"supported":47,"title":451,"abstract":452,"tags":453},[59],[31,59],[27,31],{"code":320,"score":13,"supported":47,"title":455,"abstract":456,"tags":459},[],[323,457,324,458],"边缘计算","edge computing",[323],{"code":436,"score":227,"supported":47,"title":461,"abstract":462,"tags":465},[],[463,464,437],"灌溉","水资源",[],{"code":467,"score":468,"supported":47,"title":469,"abstract":470,"tags":473},"H7",7,[],[471,472],"气候变化","climate change",[428],{"code":475,"score":94,"supported":47,"title":476,"abstract":477,"tags":480},"H2",[],[478,479],"土壤","soil",[],{"code":482,"score":71,"supported":47,"title":483,"abstract":484,"tags":486},"A7",[],[485],"间作",[],{"code":488,"score":71,"supported":47,"title":489,"abstract":490,"tags":492},"F3",[],[491],"养分管理",[],[494,495],"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":497,"openalex_id":499,"authors":500,"venue":419,"cited_by_count":84,"oa_url":416,"card":511,"direction":353,"ingested_from":91},"W7223121456",[501,504,506,508],{"name":502,"orcid":503},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":505,"orcid":9},"Oluwadare OYEBODE",{"name":507,"orcid":9},"Iretiayo Adelaiye",{"name":509,"orcid":510},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":512,"method":513,"finding":514,"direction":89,"opportunity":515},"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"]