[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2068":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":48},2068,"Leakage-Proof Reliability-Aware Deep Learning Framework for Rice Leaf Disease Diagnosis","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p113","Timely and accurate diagnosis of rice leaf diseases is significant in order to achieve reduced loss and precision agriculture management. In this paper, a leakage-aware and reliability-oriented deep learning framework is proposed to classify rice leaf diseases based on the Mendeley rice leaf disease dataset. In order to avoid the problem of similar and duplicate images in the training and validation set, the framework incorporates perceptual hashing and stratified group K-fold cross-validation. The evaluation measures the accuracy of the in-domain classification, robustness to controlled image corruption, evaluation of the Expected Calibration Error and temperature scaling methods, selective prediction using risk-coverage analysis, external-dataset validation, and computational efficiency profiling. The in-domain evaluation resulted in an almost perfect score. The accuracy achieved by the cross-validation method applied to the folds evaluated showed high values, while the external UCI rice leaf dataset presented significantly lower values, with an accuracy of 54.17% and macro-F1 of 48.98%. This contrast suggests that high in-domain accuracy should be considered with caution if there is a domain shift. In addition, efficiency and reliability analysis indicated that mid-scale convolutional models, especially ResNet-based models, achieved a better trade-off between predictive performance and computational cost compared to larger high-capacity models. The suggested assessment scheme in the proposed framework then focuses not only on accuracy but also on deployment-oriented evaluation in the context of rice leaf disease diagnosis with the help of AI.","及时、准确地诊断水稻叶片病害对于减少损失和实现精准农业管理具有重要意义。本文基于Mendeley水稻叶片病害数据集，提出了一种感知泄漏且面向可靠性的深度学习框架，用于水稻叶片病害分类。为避免训练集与验证集中出现相似和重复图像的问题，该框架引入了感知哈希（perceptual hashing）和分层分组K折交叉验证。评估内容包括域内分类准确率、对受控图像损坏的鲁棒性、期望校准误差（Expected Calibration Error）与温度缩放方法的评估、基于风险-覆盖率分析的选择性预测、外部数据集验证以及计算效率分析。域内评估取得了近乎完美的分数。应用于各折评估的交叉验证方法所达到的准确率较高，而外部UCI水稻叶片数据集则呈现出显著较低的值，准确率为54.17%，宏F1为48.98%。这一对比表明，若存在域偏移，高域内准确率应谨慎看待。此外，效率与可靠性分析表明，中等规模的卷积模型，尤其是基于ResNet的模型，相较于更大的高容量模型，在预测性能与计算成本之间实现了更好的权衡。因此，所提框架中的建议评估方案不仅关注准确率，还关注在人工智能辅助水稻叶片病害诊断背景下面向部署的评估。",null,"International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,17,9,1,"提出防数据泄漏与可靠性评估的水稻叶病诊断框架，外部数据集准确率骤降至54.17%，对农业AI落地评估有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","深度学习","水稻病害","模型可靠性",0,"10.14445\u002F22315381\u002Fijett-v74i8p113",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":40,"card":41,"direction":45,"ingested_from":47},"W7212016871",[36,38],{"name":37,"orcid":9},"Sujeet Kumar",{"name":39,"orcid":9},"Prajeet Sharma","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P113.pdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"提出防泄漏、可靠性导向的水稻叶病诊断深度学习框架，并系统评估其泛化与部署表现。","感知哈希与分层组K折交叉验证，结合校准、选择性预测及外部数据集验证。","域内准确率近完美，但外部UCI数据集仅54.17%，域偏移下高精度需谨慎。","农业人工智能与决策模型","可研究跨域自适应与轻量模型校准，提升叶病诊断在真实田间场景的泛化可靠性。","openalex","2026-09-10T23:30:43.799587Z"]