用CNN模型精准识别水稻病害，帮农民实时做决策。
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
- 对比8种CNN模型在水稻病叶图像上的分类表现。
- MobileNetV2达95.83%准确率，优于其他模型。
- 集成到Tkinter界面，支持拍照或视频实时诊断。
本研究提出一种基于8种卷积神经网络（CNN）算法的水稻病害分类新方法，推动精准农业发展。通过集成ResNet-50、InceptionV3、VGG16、MobileNetv2等先进迁移学习模型，并结合UCI数据集，构建了基于Tkinter的多功能应用，为农户提供直观界面与实时病害预测及个性化建议。实验结果显示，ResNet-50准确率为75%，DenseNet121达90%，VGG16为84%，MobileNetV2高达95.83%，DenseNet169为91.61%，InceptionV3为86%。而VGG19和Nasnet分别出现70%与80.02%的过拟合表现，ResNet101仅54%，EfficientNetB0仅为33%。最终，将训练好的MobileNetV2模型成功部署至Tkinter图形界面，支持图像上传与实时视频捕捉进行病害判断。
原文摘要 · Abstract (English)
This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, and MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101, only an accuracy of 54% could be achieved, along with only 33% on efficientNetB0. A MobileNetV2-trained model was successfully deployed on a TKinter GUI application to make predictions using image or real-time video capture.
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