[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2048":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":49},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",0,"10.38094\u002Fjastt711230",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":41,"direction":47,"ingested_from":48},"W7212078878",[37,39],{"name":38,"orcid":9},"Sowmya B",{"name":40,"orcid":9},"Guruprasad S",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"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"]