基于卷积神经网络的图像分类分析：米叶病预测的多功能应用及对农户的建议

本研究提出了一种 improve rice disease classification 的 novel method，using 8 different convolutional neural network (CNN) 算法，这 will further the field of precision agriculture。Tkinter-based application 提供了 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 和 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。