[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2048":3,"related-2048":103},{"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":45,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":102},2048,"Optimized Inception-v4 CNN Combined with YOLOv8 andXGBoost for Tomato Plant Disease Recognition and Classification","https:\u002F\u002Fdoi.org\u002F10.38094\u002Fjastt711230","This study proposes an integrated hybrid pipeline approach which is the combination of an optimized Inception-v4 Convolutional Neural Network (CNN), YOLOv8 (You Only Look Once version 8) and extreme gradient boosting (XG Boost) models to detect and classify Tomato plant diseases effectively and accurately. The proposed methodology is based on the fine-grained localization ability of the YOLOv8 model to accurately localize the affected area of the leaf, multi-scale deep feature extraction ability of the optimized Inceptionv4 CNN, and XG Boost to reduce dimensions and optimize features. The hybrid model combines the Inception-v4 CNN, YOLOv8, and XG Boost models with an average CPU processing time of about 200 milliseconds per image, the model performs better in terms of computational efficiency, robustness, and faster prediction. Experimental tests on Plant Village show that the model outperforms the state of the art in 10 disease categories. Specifically, the integrated hybrid system achieved high precision, recall, F1-score, mean Average Precision (m AP), and its training accuracy was more than 0.98 (up to 0.9969), and the testing accuracy was more than 0.96 (up to 0.9695). The hybrid framework shows good reliability even for diseases that are visually similar, such as bacterial spot, early blight, late blight, mosaic virus and yellow leaf curl virus. The optimization of batch size to 32, along with the tuning of the learning rate further improved the stability of training, convergence speed and the generalization of the overall model. The proposed system holds promise for real-time, scalable, and sustainable precision agriculture, aiming for early disease detection and yield protection. Future work includes incorporating Internet of Things (IoT) edge devices, in field environmental monitoring, multimodal data integration and applying Explainable Artificial Intelligence (EAI) techniques to enhance model interpretability for end-users.","本研究提出了一种集成的混合流水线方法，将优化的Inception-v4卷积神经网络（CNN）、YOLOv8（You Only Look Once version 8）和极端梯度提升（XG Boost）模型相结合，以有效且准确地检测和分类番茄植株病害。所提方法基于YOLOv8模型的细粒度定位能力来准确定位叶片受感染区域、优化的Inception-v4 CNN的多尺度深层特征提取能力，以及XG Boost用于降维和特征优化。该混合模型将Inception-v4 CNN、YOLOv8和XG Boost模型结合在一起，每幅图像的平均CPU处理时间约为200毫秒，模型在计算效率、鲁棒性和更快预测方面表现更优。在Plant Village上的实验测试表明，该模型在10个病害类别上优于现有最先进方法。具体而言，该集成混合系统实现了较高的精确率、召回率、F1分数、平均精度均值（mAP），其训练准确率超过0.98（最高达0.9969），测试准确率超过0.96（最高达0.9695）。即使对于视觉上相似的病害，如细菌性斑点病、早疫病、晚疫病、花叶病毒和黄花叶卷曲病毒，该混合框架也表现出良好的可靠性。将批量大小优化为32，并调整学习率，进一步提高了训练稳定性、收敛速度和整体模型的泛化能力。所提系统有望用于实时、可扩展和可持续的精准农业，旨在实现早期病害检测和产量保护。未来工作包括整合物联网（IoT）边缘设备、田间环境监测、多模态数据融合，以及应用可解释人工智能（EAI）技术以增强模型对最终用户的可解释性。",null,"Journal of Applied Science and Technology Trends","2026-09-09T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,18,12,9,1,"提出YOLOv8+Inception-v4+XGBoost混合模型用于番茄病害识别，准确率与效率数据扎实，对农业AI病害检测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","病虫害识别","精准农业","番茄种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,66,73],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},15,true,[55],"tomato",[57,55],"番茄",[31],{"code":40,"score":21,"supported":53,"title":60,"abstract":61,"tags":65},[],[62,63,64],"物联网","internet of things","IoT",[],{"code":67,"score":68,"supported":53,"title":69,"abstract":70,"tags":72},"I1",8,[],[30,71],"precision agriculture",[27,30],{"code":74,"score":75,"supported":53,"title":76,"abstract":77,"tags":79},"I2",5,[],[78],"artificial intelligence",[28,29],[81,82],"农业人工智能 病虫害识别 智慧农业 番茄种植","农业人工智能 病虫害识别","农业人工智能病虫害识别智慧农业番茄种植-2048",2,"10.38094\u002Fjastt711230",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":93,"oa_url":6,"card":94,"direction":100,"ingested_from":101},"W7212078878",[89,91],{"name":90,"orcid":9},"Sowmya B",{"name":92,"orcid":9},"Guruprasad S",0,{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"提出YOLOv8+Inception-v4+XGBoost混合模型，实现番茄叶片病害精准检测与分类。","YOLOv8定位病斑，Inception-v4提取多尺度特征，XGBoost降维","10类病害识别精度超现有方法，训练准确率0.9969，测试0.9695，单图CPU约200毫秒。","农业人工智能与决策模型","可探索IoT边缘部署、多模态数据融合与可解释AI，提升田间实时病害诊断可信度。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:14.735468Z",{"total":104,"page":22,"page_size":104,"items":105},6,[106,202,297,361,413,478],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":130,"classification":133,"search_phrases":179,"slug":182,"view_count":93,"doi":9,"paper":183,"created_at":201},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":20,"substance":116,"depth":117,"authority":21,"freshness":68,"relevant":22,"comment":118},21,17,"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[120],{"name":112,"url":109},[27,28,122,123,124],"农业物联网","温室环境","小气候预测",{"code":126,"slug":127,"name":128,"path":129,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[131,132],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":134},{"candidates":135,"agricultural_evidence":53},[136,140,147,154,159,165,172],{"code":40,"score":52,"supported":53,"title":137,"abstract":138,"tags":139},[64],[62,64],[122],{"code":126,"score":141,"supported":53,"title":142,"abstract":144,"tags":146},14,[143],"greenhouse",[145,143],"温室",[],{"code":33,"score":21,"supported":53,"title":148,"abstract":149,"tags":153},[],[57,150,151,55,152],"辣椒","黄瓜","cucumber",[],{"code":67,"score":155,"supported":53,"title":156,"abstract":157,"tags":158},7,[],[30,71],[27],{"code":74,"score":155,"supported":53,"title":160,"abstract":161,"tags":164},[],[162,163],"深度学习","deep learning",[28],{"code":166,"score":104,"supported":53,"title":167,"abstract":168,"tags":171},"H2",[],[169,170],"土壤","soil",[],{"code":173,"score":104,"supported":53,"title":174,"abstract":175,"tags":178},"H3",[],[176,177],"灌溉","irrigation",[],[180,181],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":184,"authors":185,"venue":112,"cited_by_count":93,"oa_url":9,"card":196,"direction":100,"ingested_from":101},"W7222909837",[186,188,190,192,194],{"name":187,"orcid":9},"Siman Emmanuel*",{"name":189,"orcid":9},"Edward Onyebueke Agu",{"name":191,"orcid":9},"Musa Gani",{"name":193,"orcid":9},"Oladunjoye John Abiodun",{"name":195,"orcid":9},"Anagu Emmanuel John",{"tldr":197,"method":198,"finding":199,"direction":100,"opportunity":200},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":208,"source_url":205,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":210,"sources":212,"tags":214,"primary_topic":216,"secondary_topics":217,"classification":227,"search_phrases":275,"slug":278,"view_count":22,"doi":279,"paper":280,"created_at":296},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",{"impact":19,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":211},"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[213],{"name":208,"url":205},[27,28,29,30,215],"蔬菜种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[218,222],{"code":74,"slug":219,"name":220,"path":221,"parent_code":44},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":223,"slug":224,"name":225,"path":226,"parent_code":44},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":46,"locked":14,"version":47,"method":48,"evidence":228},{"candidates":229,"agricultural_evidence":53},[230,237,242,249,253,260,264,268],{"code":74,"score":231,"supported":53,"title":232,"abstract":233,"tags":236},19,[78],[234,235,78],"机器学习","machine learning",[28,29],{"code":33,"score":19,"supported":53,"title":238,"abstract":240,"tags":241},[239],"vegetable",[35,57,150,239,55],[215],{"code":223,"score":21,"supported":53,"title":243,"abstract":244,"tags":248},[],[245,246,247],"地理信息","GIS","geographic information",[],{"code":67,"score":68,"supported":53,"title":250,"abstract":251,"tags":252},[],[30,71],[27,30],{"code":254,"score":104,"supported":53,"title":255,"abstract":256,"tags":259},"F4",[],[257,258],"农药","pesticide",[],{"code":166,"score":104,"supported":53,"title":261,"abstract":262,"tags":263},[],[169,170],[],{"code":173,"score":104,"supported":53,"title":265,"abstract":266,"tags":267},[],[176,177],[],{"code":269,"score":270,"supported":53,"title":271,"abstract":272,"tags":274},"I3",3,[],[273],"multispectral",[],[276,277],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":279,"openalex_id":281,"authors":282,"venue":208,"cited_by_count":93,"oa_url":9,"card":291,"direction":98,"ingested_from":101},"W7215128283",[283,285,287,289],{"name":284,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":286,"orcid":9},"Dr. SK Kshirsagar",{"name":288,"orcid":9},"MG Agale",{"name":290,"orcid":9},"GS Shinde",{"tldr":292,"method":293,"finding":294,"direction":100,"opportunity":295},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","2026-10-02T23:30:40.356457Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":9,"source_name":303,"source_url":300,"published_at":304,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":309,"tags":311,"primary_topic":313,"secondary_topics":314,"classification":316,"search_phrases":344,"slug":346,"view_count":22,"doi":347,"paper":348,"created_at":360},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":19,"substance":18,"depth":117,"authority":307,"freshness":68,"relevant":22,"comment":308},13,"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[310],{"name":303,"url":300},[27,28,29,30,312],"作物推荐",{"code":74,"slug":219,"name":220,"path":221,"parent_code":44},[315],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":317},{"candidates":318,"agricultural_evidence":53},[319,323,328,332,339],{"code":74,"score":231,"supported":53,"title":320,"abstract":321,"tags":322},[235],[162,234,235,163],[28,29],{"code":67,"score":17,"supported":53,"title":324,"abstract":326,"tags":327},[325],"smart agriculture",[30,71],[27,30],{"code":40,"score":21,"supported":53,"title":329,"abstract":330,"tags":331},[],[62,63,64],[],{"code":333,"score":104,"supported":53,"title":334,"abstract":335,"tags":338},"A8",[],[336,337],"粮食安全","food security",[],{"code":254,"score":270,"supported":53,"title":340,"abstract":341,"tags":343},[],[342],"除草",[],[345,82],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":347,"openalex_id":349,"authors":350,"venue":303,"cited_by_count":93,"oa_url":300,"card":355,"direction":100,"ingested_from":101},"W7214889993",[351,353],{"name":352,"orcid":9},"M. Umamaheswari",{"name":354,"orcid":9},"Kavitha D",{"tldr":356,"method":357,"finding":358,"direction":98,"opportunity":359},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","2026-10-01T23:30:09.158196Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":373,"tags":375,"primary_topic":376,"secondary_topics":377,"classification":379,"search_phrases":395,"slug":397,"view_count":270,"doi":398,"paper":399,"created_at":412},2158,"EfficientNet-CBAM-prototype: an attention-guided EfficientNet framework with dynamic prototype representation for tomato disease classification","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1918175","Introduction In the field of precision agriculture, one of the major hurdles is the early and accurate identification of plant diseases. Farmers may face serious irreversible loss in yield if there is a delay in diagnosis by even a few days. The CNN model has helped in improving the classification of plant diseases but faces difficulty in distinguishing fine-grained symptoms, has limited generalizability, and is not easily interpretable. Though the deep CNN model performs well in classifying plant diseases, the current methods have several flaws. Most importantly, existing algorithms misclassify visually complex samples due to their limited spatial differentiation of disease-specific morphological features, including lesion borders and necrotic regions. Classification reliability is further compromised by low inter-class embedding separability for visually comparable illness phenotypes. Apart from these representational problems, training instability is still a major problem for attention-based models. Combining randomly initialized attention modules with pretrained backbone networks causes this instability, which still limits practical application. Methods We suggest EfficientNet-CBAM-Prototype (ECP-Net), a unique end-to-end deep learning architecture, to overcome these constraints. Three complementary techniques are combined into a single framework by ECP-Net. First, parameter-efficient multi-scale feature extraction is done using an EfficientNetB0 backbone. Second, joint channel-wise and spatial feature recalibration is performed using a stabilized convolutional block attention module (CBAM). Third, a dynamic prototype memory layer uses cosine similarity-based categorization and exponential moving average (EMA) updates to maintain class-representative embedding vectors. To overcome the instability caused by randomly initialized attention weights, we further propose a two-phase training strategy wherein CBAM is frozen during phase 1 to allow prototype stabilization and then jointly fine-tuned with the learning rate in phase 2. Results Evaluated on the PlantVillage tomato subset comprising 10 disease classes across a class-balanced split of 10,000 training, 500 validation, and 500 test samples, ECP-Net achieves 98.6% test accuracy, 98.59% F1-score, and 98.65% precision with only 4.80M parameters and 85.04 ms average inference time. These results outperformed baselines including VGG16 (97.00%), ResNet50 (81.20%), MobileNetV2 (81.20%), and CNN (70.00%). Discussion Generalization is further validated on 35 real-field tomato leaf images captured under natural, uncontrolled conditions, confirming practical deployment potential.","引言 在精准农业领域，植物病害的早期准确识别是主要难题之一。若诊断延迟哪怕几天，农民可能面临严重的不可逆产量损失。CNN模型有助于改进植物病害分类，但在区分细粒度症状方面存在困难，泛化能力有限，且不易解释。尽管深度CNN模型在植物病害分类中表现良好，当前方法仍存在若干缺陷。最重要的是，现有算法由于对病害特异性形态特征（包括病斑边缘和坏死区域）的空间区分能力有限，会对视觉上复杂的样本产生误分类。对于视觉上相似的病害表型，类间嵌入可分性低，进一步损害了分类可靠性。除这些表征问题外，训练不稳定性仍是基于注意力模型的主要问题。随机初始化的注意力模块与预训练骨干网络相结合会导致这种不稳定性，这仍然限制了实际应用。方法 我们提出EfficientNet-CBAM-Prototype（ECP-Net），一种新颖的端到端深度学习架构，以克服这些限制。ECP-Net将三种互补技术结合到一个统一框架中。首先，使用EfficientNetB0骨干网络进行参数高效的多尺度特征提取。其次，使用稳定化的卷积块注意力模块（CBAM）进行通道与空间的联合特征重校准。第三，动态原型记忆层使用基于余弦相似度的分类和指数移动平均（EMA）更新来维护类代表性嵌入向量。为克服随机初始化注意力权重引起的不稳定性，我们进一步提出两阶段训练策略，其中CBAM在第一阶段冻结以使原型稳定，然后在第二阶段以学习率联合微调。结果 在PlantVillage番茄子集上评估，该子集包含10个病害类别，按类别均衡划分的10,000个训练样本、500个验证样本和500个测试样本，ECP-Net达到98.6%的测试准确率、98.59%的F1分数和98.65%的精确率，仅需4.80M参数和85.04 ms平均推理时间。这些结果优于基线，包括VGG16（97.00%）、ResNet50（81.20%）、MobileNetV2（81.20%）和CNN（70.00%）。","Frontiers in Plant Science","2026-09-10T00:00:00Z",81,{"impact":19,"substance":371,"depth":19,"authority":141,"freshness":21,"relevant":22,"comment":372},22,"提出注意力引导与动态原型结合的番茄病害识别框架，在公开数据集上取得98.6%准确率并完成真实田间图像验证，方法新颖、数据扎实，对作物病害智能诊断有实用参考价值。",[374],{"name":367,"url":364},[27,28,162,29,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[378],{"code":74,"slug":219,"name":220,"path":221,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":380},{"candidates":381,"agricultural_evidence":53},[382,387,391],{"code":33,"score":19,"supported":53,"title":383,"abstract":384,"tags":386},[55],[57,385,55],"茄子",[31],{"code":74,"score":21,"supported":53,"title":388,"abstract":389,"tags":390},[],[162,163],[28,162,29],{"code":67,"score":155,"supported":53,"title":392,"abstract":393,"tags":394},[],[30,71],[27],[396,82],"农业人工智能 病虫害识别 智慧农业 深度学习","农业人工智能病虫害识别智慧农业深度学习-2158","10.3389\u002Ffpls.2026.1918175",{"doi":398,"openalex_id":400,"authors":401,"venue":367,"cited_by_count":93,"oa_url":364,"card":406,"direction":411,"ingested_from":101},"W7212120374",[402,404],{"name":403,"orcid":9},"E Jansi",{"name":405,"orcid":9},"Kavitha BR",{"tldr":407,"method":408,"finding":409,"direction":98,"opportunity":410},"提出ECP-Net，用注意力与动态原型提升番茄病害分类精度与可解释性。","EfficientNetB0+稳定CBAM+动态原型记忆层，两阶段训练策略。","在PlantVillage番茄10类上达98.6%准确率，仅4.80M参数，优于多个基线。","可探索原型可解释性在田间复杂背景与跨域病害识别中的泛化能力。","农业遥感与作物表型","2026-09-11T23:30:25.043704Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":9,"content":418,"source_name":419,"source_url":9,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":424,"tags":426,"primary_topic":432,"secondary_topics":433,"classification":439,"search_phrases":473,"slug":476,"view_count":22,"doi":9,"paper":9,"created_at":477},1035,"Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production","https:\u002F\u002Fjournal-of-social-education.org\u002Findex.php\u002FJorunal\u002Farticle\u002Fview\u002F1226","原文标题:Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production。作者:Zeeshan Ahmed、Muhammad Taimur Maqbool Muhammad、Abbas Khan Muhammad、Zain Ul Abideen、Abdul Haq。DOI: 10.63163\u002Fjpehss.v4i1.1226。该综述综合分析了精准农业(PA)和基于物联网(IoT)的智能灌溉在温室蔬菜生产中的集成,涉及土壤湿度、EC、pH、微气候监测器等物联网传感器与数据分析(AI\u002FML模型)及自动执行器在番茄、黄瓜、辣椒等作物实时资源管理中的整合。关键组件包括使用ZigBee\u002FLoRa协议的无线传感器网络、低延迟边缘\u002F雾计算和预测算法(如LSTM、SVM),可减少20-50%用水量,同时提高10-30%产量和养分效率。来自中国、欧洲和印度的案例展示了变量灌溉(VRI)和施肥系统的实际应用,实现高达40%的能源节约和最小化淋失。","## Authors\n\n*    Zeeshan Ahmed  Department of Water Resources Management, Lasbela University of Agriculture, water and Marine Sciences, Uthal. zeesmile1999@gmail.com  Author \n*    Muhammad Taimur Maqbool  Department of Agronomy, University of Agriculture Faisalabad. taimuragronomist@gmail.com  Author \n*    Muhammad Abbas Khan  Department of Horticulture, Balochistan Agriculture College Quetta. muhammadabbaskhan1121@gmail.com  Author \n*    Zain Ul Abideen  Department of Agronomy, Agriculture University Peshawar zainkhanbajaur@gmail.com  Author \n*    Abdul Haq  Agriculture Engineer at Department of Agriculture research institute Balochistan Quetta. abdulhaqkakar54@gmail.com  Author \n\n## DOI:\n\n[https:\u002F\u002Fdoi.org\u002F10.63163\u002Fjpehss.v3i4.1226](https:\u002F\u002Fdoi.org\u002F10.63163\u002Fjpehss.v3i4.1226)\n## Abstract\n\nPrecision agriculture (PA) and Internet of Things (IoT)-based smart irrigation represent transformative technologies for optimizing greenhouse vegetable production, addressing global challenges like population growth (projected 9.7 billion by 2050), water scarcity, and climate variability. This review synthesizes the integration of IoT sensors (soil moisture, EC, pH, microclimate monitors) with data analytics (AI\u002FML models) and automated actuators for real-time resource management in crops such as tomatoes, cucumbers, and peppers. Key components include wireless sensor networks (WSNs) using protocols like ZigBee\u002FLoRa for data transmission, edge\u002Ffog computing for low-latency processing, and predictive algorithms (e.g., LSTM, SVM) that reduce water use by 20–50% while enhancing yield (10–30%) and nutrient efficiency. Case studies from China, Europe, and India demonstrate practical implementations, such as variable rate irrigation (VRI) and fertigation systems, achieving up to 40% energy savings and minimized leaching. Challenges encompass high initial costs, data security, and sensor accuracy in humid environments, with future directions emphasizing AI-driven digital twins, blockchain for traceability, and hybrid renewable energy integration for sustainable, resilient greenhouse systems.\n\n## Published\n\n2025-12-30\n\n## Issue\n\n## Section\n\nBiological Sciences","Journal of Physical Education Health and Social Sciences 2026, 4(1): 781-794","2026-08-21T16:00:00Z",56,{"impact":20,"substance":19,"depth":17,"authority":68,"freshness":84,"relevant":22,"comment":423},"综述性论文，涵盖精准农业与IoT智能灌溉在温室蔬菜中的应用，信息较全面，但来源期刊权威性一般，且发表时间较早。",[425],{"name":419,"url":416},[427,30,428,429,430,431],"智能灌溉","温室蔬菜","LSTM LoRa","变量灌溉","施肥系统",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[434,435],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":173,"slug":177,"name":436,"path":437,"parent_code":438},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":440},{"candidates":441,"agricultural_evidence":53},[442,446,451,455,459,463,469],{"code":33,"score":117,"supported":53,"title":443,"abstract":444,"tags":445},[239],[35,57,150,151,239],[],{"code":40,"score":117,"supported":53,"title":447,"abstract":448,"tags":450},[64],[62,449,64],"无线传感",[],{"code":173,"score":52,"supported":53,"title":452,"abstract":453,"tags":454},[177],[176,177],[427],{"code":67,"score":52,"supported":53,"title":456,"abstract":457,"tags":458},[71],[30,71],[30],{"code":126,"score":141,"supported":53,"title":460,"abstract":461,"tags":462},[143],[145,143],[],{"code":464,"score":270,"supported":53,"title":465,"abstract":466,"tags":468},"F3",[],[467],"施肥",[],{"code":166,"score":270,"supported":53,"title":470,"abstract":471,"tags":472},[],[169],[],[474,475],"变量灌溉 施肥系统 智能灌溉 温室蔬菜","变量灌溉 施肥系统","变量灌溉施肥系统智能灌溉温室蔬菜-1035","2026-08-28T00:07:02.279441Z",{"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":493,"secondary_topics":494,"classification":497,"search_phrases":543,"slug":546,"view_count":93,"doi":547,"paper":548,"created_at":566},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":19,"substance":371,"depth":19,"authority":307,"freshness":21,"relevant":22,"comment":488},"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。",[490],{"name":484,"url":481},[27,28,62,30,492],"气候韧性",{"code":74,"slug":219,"name":220,"path":221,"parent_code":44},[495,496],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":173,"slug":177,"name":436,"path":437,"parent_code":438},{"status":46,"locked":14,"version":47,"method":48,"evidence":498},{"candidates":499,"agricultural_evidence":53},[500,506,510,516,521,528,532,538],{"code":74,"score":19,"supported":53,"title":501,"abstract":502,"tags":505},[78],[162,234,503,235,163,504],"计算机视觉","computer vision",[28],{"code":67,"score":17,"supported":53,"title":507,"abstract":508,"tags":509},[71],[30,71],[27,30],{"code":40,"score":13,"supported":53,"title":511,"abstract":512,"tags":515},[],[62,513,64,514],"边缘计算","edge computing",[62],{"code":173,"score":21,"supported":53,"title":517,"abstract":518,"tags":520},[],[176,519,177],"水资源",[],{"code":522,"score":155,"supported":53,"title":523,"abstract":524,"tags":527},"H7",[],[525,526],"气候变化","climate change",[492],{"code":166,"score":104,"supported":53,"title":529,"abstract":530,"tags":531},[],[169,170],[],{"code":533,"score":270,"supported":53,"title":534,"abstract":535,"tags":537},"A7",[],[536],"间作",[],{"code":464,"score":270,"supported":53,"title":539,"abstract":540,"tags":542},[],[541],"养分管理",[],[544,545],"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":547,"openalex_id":549,"authors":550,"venue":484,"cited_by_count":93,"oa_url":481,"card":561,"direction":100,"ingested_from":101},"W7223121456",[551,554,556,558],{"name":552,"orcid":553},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":555,"orcid":9},"Oluwadare OYEBODE",{"name":557,"orcid":9},"Iretiayo Adelaiye",{"name":559,"orcid":560},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":562,"method":563,"finding":564,"direction":98,"opportunity":565},"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"]