[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2179":3,"related-2179":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":96},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。",null,"Discover Artificial Intelligence","2026-09-10T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},16,20,12,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","机器学习","杂草识别","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,59,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":50,"tags":52},27,true,[48,49],"machine learning","deep learning",[51,28,48,49],"深度学习",[27,28],{"code":54,"score":17,"supported":46,"title":55,"abstract":57,"tags":58},"I1",[56],"precision agriculture",[30,56],[26,30],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"A3",6,[],[64,65],"玉米","maize",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"F4",[],[],[29],[73,74],"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","农业人工智能智慧农业机器学习杂草识别-2179",3,"10.1007\u002Fs44163-026-02195-y",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7212111551",[81,84,86],{"name":82,"orcid":83},"Rajeev Kumar","https:\u002F\u002Forcid.org\u002F0000-0001-8414-3778",{"name":85,"orcid":9},"P. K. Singh",{"name":87,"orcid":9},"Rohit Kumar Tiwari",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业人工智能与决策模型","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。","openalex","2026-09-11T23:30:52.642134Z",{"total":61,"page":21,"page_size":61,"items":98},[99,183,246,301,366,422],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":113,"tags":115,"primary_topic":117,"secondary_topics":118,"classification":119,"search_phrases":140,"slug":143,"view_count":88,"doi":144,"paper":145,"created_at":182},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":109,"substance":18,"depth":110,"authority":111,"freshness":20,"relevant":21,"comment":112},18,17,14,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[114],{"name":105,"url":102},[26,27,28,30,116],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":120},{"candidates":121,"agricultural_evidence":46},[122,128,135],{"code":32,"score":123,"supported":46,"title":124,"abstract":125,"tags":127},11,[],[28,48,126],"artificial intelligence",[27,28],{"code":129,"score":61,"supported":46,"title":130,"abstract":131,"tags":134},"H2",[],[132,133],"土壤","soil",[],{"code":54,"score":136,"supported":14,"title":137,"abstract":138,"tags":139},2,[],[],[26,30],[141,142],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":144,"openalex_id":146,"authors":147,"venue":105,"cited_by_count":88,"oa_url":102,"card":177,"direction":93,"ingested_from":95},"W7222866367",[148,151,154,157,160,163,166,169,171,174],{"name":149,"orcid":150},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":152,"orcid":153},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":155,"orcid":156},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":158,"orcid":159},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":161,"orcid":162},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":164,"orcid":165},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":167,"orcid":168},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":170,"orcid":9},"M. Z. Walkup",{"name":172,"orcid":173},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":175,"orcid":176},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":178,"method":179,"finding":180,"direction":93,"opportunity":181},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":191,"is_selected":14,"score":192,"score_detail":193,"sources":198,"tags":202,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":229,"slug":232,"view_count":136,"doi":233,"paper":234,"created_at":245},3271,"A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896343","Selecting the crop best matched to a field’s soil and climate is one of the highest-leverage decisions in agriculture, and one that farmers have traditionally made by intuition or inherited practice. Soil pH together with nitrogen, phosphorus and potassium concentration, and the local temperature, humidity and rainfall regime, jointly determine which crop will flourish and which will fail, and the relationship between those variables and crop performance is neither linear nor independent. Machine learning has therefore become the dominant approach to automated crop recommendation. This paper reviews the field across four technique families — classical supervised learning, ensemble and boosting methods, deep learning and metaheuristic hybrids, and IoT and deployment-oriented systems — and compares twenty-four representative studies published between 2016 and 2026 in terms of method, data source, reported accuracy, advantage and limitation. A generic seven-stage recommendation pipeline is presented and each family is situated within it. The comparison shows that reported accuracy on the standard nutrient-and-climate benchmark has converged in a narrow band between roughly 98 and 99.5 per cent, that boosting and ensemble methods occupy the upper part of that band, and that further gains on the benchmark are no longer the binding constraint on the field. The gaps that remain open are instead the absence of socio-economic and market variables from the decision, the lack of region-specific and long-horizon environmental validation, dataset narrowness and geographic bias, limited interpretability, and the accessibility of these systems to small and resource-poor farmers. These are consolidated into a set of research directions for future work.","选择与田块土壤和气候最匹配的作物是农业中杠杆效应最高的决策之一，而农民传统上依靠直觉或世代相传的经验来做出这一决策。土壤pH值以及氮、磷、钾浓度，加上当地的气温、湿度和降雨状况，共同决定了哪种作物能够茁壮成长、哪种会歉收，而这些变量与作物表现之间的关系既非线性也非相互独立。因此，机器学习已成为自动化作物推荐的主流方法。本文从四个技术族系——经典监督学习、集成与提升方法、深度学习与元启发式混合方法，以及物联网与面向部署的系统——对该领域进行了综述，并从方法、数据来源、报告精度、优势和局限性方面比较了2016年至2026年间发表的二十四项代表性研究。本文提出了一个通用的七阶段推荐流程，并将每个技术族系置于该流程中加以定位。比较结果表明，在标准养分与气候基准上的报告精度已收敛于约98%至99.5%的狭窄区间内，提升与集成方法占据该区间的上端，而在该基准上进一步提升已不再是该领域的约束瓶颈。真正尚未填补的空白在于：决策中缺乏社会经济和市场变量，缺少针对特定区域和长期环境验证，数据集狭窄且存在地理偏差，可解释性有限，以及这些系统对小型和资源匮乏农户的可及性不足。这些空白被归纳为未来工作的一系列研究方向。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,78,{"impact":17,"substance":194,"depth":109,"authority":195,"freshness":196,"relevant":21,"comment":197},22,13,9,"系统综述24项研究并指出基准精度已趋饱和，真正瓶颈转向社会经济变量与可解释性，对智慧农业选种方向有参考价值。",[199,200],{"name":189,"url":186},{"name":189,"url":201},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[26,27,28,30,203],"作物推荐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,213,218,222],{"code":32,"score":123,"supported":46,"title":210,"abstract":211,"tags":212},[],[51,28,48,49],[27,28],{"code":54,"score":13,"supported":46,"title":214,"abstract":216,"tags":217},[215],"smart agriculture",[],[26,30],{"code":129,"score":61,"supported":46,"title":219,"abstract":220,"tags":221},[],[132,133],[],{"code":223,"score":61,"supported":46,"title":224,"abstract":225,"tags":228},"I5",[],[226,227],"物联网","IoT",[],[230,231],"农业人工智能 作物推荐 智慧农业 机器学习","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":233,"openalex_id":235,"authors":236,"venue":189,"cited_by_count":88,"oa_url":186,"card":239,"direction":244,"ingested_from":95},"W7214002748",[237],{"name":238,"orcid":9},"Prof. Nagendra Patel Sahil Verma",{"tldr":240,"method":241,"finding":242,"direction":93,"opportunity":243},"系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。","综述监督学习、集成提升、深度学习与元启发式、物联网部署四类方法及七阶段流程。","标准基准精度收敛于98%-99.5%，提升集成法最优，但精度已非领域瓶颈。","将社会经济与市场变量、区域长期环境验证及可解释性纳入作物推荐，服务小农户。","智慧农业 \u002F 农业物联网","2026-09-23T23:30:09.369987Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":10,"source_url":249,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":279,"slug":282,"view_count":76,"doi":283,"paper":284,"created_at":300},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。","2026-09-18T00:00:00Z",67,{"impact":19,"substance":109,"depth":17,"authority":195,"freshness":20,"relevant":21,"comment":255},"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。",[257],{"name":10,"url":249},[26,27,51,29,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":262},{"candidates":263,"agricultural_evidence":46},[264,271,275],{"code":32,"score":265,"supported":46,"title":266,"abstract":267,"tags":270},19,[49],[268,51,28,48,49,269],"目标检测","YOLO",[27,51],{"code":54,"score":17,"supported":46,"title":272,"abstract":273,"tags":274},[56],[30,56],[26,30],{"code":68,"score":21,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[29],[280,281],"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","10.1007\u002Fs44163-026-01916-7",{"doi":283,"openalex_id":285,"authors":286,"venue":10,"cited_by_count":88,"oa_url":249,"card":295,"direction":93,"ingested_from":95},"W7213558223",[287,289,291,293],{"name":288,"orcid":9},"Njoku Camillus Ekene",{"name":290,"orcid":9},"Francis A. Okoye",{"name":292,"orcid":9},"Ebere Uzoka Chidi",{"name":294,"orcid":9},"OGBU MARY NNENNA",{"tldr":296,"method":297,"finding":298,"direction":93,"opportunity":299},"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。","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。","2026-09-19T23:30:56.875661Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":308,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":309,"score_detail":310,"sources":312,"tags":314,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":341,"slug":343,"view_count":344,"doi":345,"paper":346,"created_at":365},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports","2026-09-17T00:00:00Z",73,{"impact":19,"substance":18,"depth":110,"authority":111,"freshness":13,"relevant":21,"comment":311},"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[313],{"name":307,"url":304},[26,27,28,30,315],"表型分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":319},{"candidates":320,"agricultural_evidence":46},[321,325,329,335],{"code":32,"score":17,"supported":46,"title":322,"abstract":323,"tags":324},[48],[28,48],[27,28],{"code":54,"score":20,"supported":46,"title":326,"abstract":327,"tags":328},[],[30,56],[26,30],{"code":330,"score":76,"supported":46,"title":331,"abstract":332,"tags":334},"B2",[],[333],"orchard",[],{"code":336,"score":76,"supported":46,"title":337,"abstract":338,"tags":340},"H7",[],[339],"气候变化",[],[342,74],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能智慧农业机器学习精准农业-2802",4,"10.1038\u002Fs41598-026-71390-9",{"doi":345,"openalex_id":347,"authors":348,"venue":307,"cited_by_count":88,"oa_url":304,"card":359,"direction":93,"ingested_from":95},"W7213449017",[349,352,354,356],{"name":350,"orcid":351},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":353,"orcid":9},"Hossein Sabouri",{"name":355,"orcid":9},"Ali Tanhaei",{"name":357,"orcid":358},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":360,"method":361,"finding":362,"direction":363,"opportunity":364},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","农业遥感与作物表型","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","2026-09-17T23:30:59.169744Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":9,"source_name":307,"source_url":369,"published_at":372,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":373,"sources":376,"tags":378,"primary_topic":380,"secondary_topics":381,"classification":382,"search_phrases":404,"slug":407,"view_count":21,"doi":408,"paper":409,"created_at":421},2338,"An optimized machine learning approach for reliable agronomic parameter prediction in precision farming systems","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71592-1","Optimized machine learning models are very important for improving predictive performance in precision agriculture because they let us analyze soil and environmental data in a data-driven way. But conventional predictive methods often can’t be used to make generalizations because agronomic data is often very variable, has nonlinear interactions, and is very different from one another. The study presents an enhanced machine learning-based predictive framework for estimating agricultural parameters utilizing structured numerical soil and environmental datasets. The framework combines systematic data preprocessing, feature selection, and hyperparameter optimization to make models more stable and reliable. The model performance was evaluated using five-fold cross-validation and standard regression metrics, including the Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The average R 2 is 0.696 ± 0.148, the RMSE is 5.160 ± 1.571, and the MAE is 4.636 ± 1.545, which shows that the model can make accurate predictions across all validation folds. Further the classification of the growth stages of spinach is done with an accuracy of 84.63% and with the precision of 85% using the proposed Hybrid ensemble model. The results show that the proposed framework works well with nonlinear agricultural data and could be used for data-driven decisions in precision farming systems.","优化的机器学习模型对于提升精准农业中的预测性能至关重要，因为它使我们能够以数据驱动的方式分析土壤和环境数据。然而，传统的预测方法往往无法用于泛化，因为农艺数据通常变异性很大、具有非线性交互作用，且彼此之间差异显著。本研究提出了一种基于增强机器学习的预测框架，利用结构化数值土壤和环境数据集来估算农业参数。该框架结合了系统化的数据预处理、特征选择和超参数优化，使模型更加稳定可靠。采用五折交叉验证和标准回归指标评估模型性能，包括决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）。平均R²为0.696 ± 0.148，RMSE为5.160 ± 1.571，MAE为4.636 ± 1.545，表明该模型在所有验证折中均能做出准确预测。此外，利用所提出的混合集成模型对菠菜生长阶段进行分类，准确率达到84.63%，精确率达到85%。结果表明，所提出的框架能够很好地处理非线性农业数据，可用于精准农业系统中的数据驱动决策。","2026-09-12T00:00:00Z",{"impact":19,"substance":109,"depth":374,"authority":111,"freshness":20,"relevant":21,"comment":375},15,"该论文提出融合预处理、特征选择与超参数优化的机器学习框架，在土壤环境数据上取得R²约0.70的预测表现并以84.63%准确率识别菠菜生长期，方法扎实但属常规模型优化，产业影响有限，可作为智慧农业技术参考。",[377],{"name":307,"url":369},[26,27,28,30,379],"土壤数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":383},{"candidates":384,"agricultural_evidence":46},[385,389,393,398],{"code":32,"score":17,"supported":46,"title":386,"abstract":387,"tags":388},[48],[28,48],[27,28],{"code":54,"score":20,"supported":46,"title":390,"abstract":391,"tags":392},[],[30,56],[26,30],{"code":129,"score":394,"supported":46,"title":395,"abstract":396,"tags":397},7,[],[132,133],[379],{"code":399,"score":76,"supported":46,"title":400,"abstract":401,"tags":403},"I6",[],[402],"agricultural data",[],[405,406],"农业人工智能 土壤数据 智慧农业 机器学习","农业人工智能 土壤数据","农业人工智能土壤数据智慧农业机器学习-2338","10.1038\u002Fs41598-026-71592-1",{"doi":408,"openalex_id":410,"authors":411,"venue":307,"cited_by_count":88,"oa_url":369,"card":416,"direction":93,"ingested_from":95},"W7212396250",[412,414],{"name":413,"orcid":9},"T. Suba",{"name":415,"orcid":9},"K. Lakshmi Joshitha",{"tldr":417,"method":418,"finding":419,"direction":93,"opportunity":420},"提出优化机器学习框架，用土壤环境数据预测农艺参数并分类菠菜生长阶段。","数据预处理、特征选择、超参数优化，五折交叉验证与混合集成模型。","回归平均R²为0.696，菠菜生长阶段分类准确率达84.63%。","可探索跨作物、跨区域迁移学习与可解释性，提升非线性农艺数据泛化能力。","2026-09-13T23:30:43.564799Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":372,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":429,"score_detail":430,"sources":432,"tags":434,"primary_topic":435,"secondary_topics":436,"classification":437,"search_phrases":459,"slug":461,"view_count":76,"doi":462,"paper":463,"created_at":477},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",75,{"impact":374,"substance":18,"depth":109,"authority":111,"freshness":20,"relevant":21,"comment":431},"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。",[433],{"name":428,"url":425},[26,27,51,29,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":438},{"candidates":439,"agricultural_evidence":46},[440,444,448,455],{"code":54,"score":17,"supported":46,"title":441,"abstract":442,"tags":443},[56],[30,56],[26,30],{"code":32,"score":17,"supported":46,"title":445,"abstract":446,"tags":447},[49],[51,49],[27,51],{"code":449,"score":111,"supported":46,"title":450,"abstract":452,"tags":454},"I7",[451],"bibliometric",[453,451],"文献计量",[],{"code":68,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[29],[460,74],"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杂草识别深度学习-2335","10.1007\u002Fs10343-026-01424-9",{"doi":462,"openalex_id":464,"authors":465,"venue":428,"cited_by_count":88,"oa_url":9,"card":472,"direction":93,"ingested_from":95},"W7212352052",[466,469],{"name":467,"orcid":468},"Furkan Ulaş","https:\u002F\u002Forcid.org\u002F0009-0002-3052-4457",{"name":470,"orcid":471},"Muhammad Aasım","https:\u002F\u002Forcid.org\u002F0000-0002-8524-9029",{"tldr":473,"method":474,"finding":475,"direction":93,"opportunity":476},"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"]