基于卷积神经网络的图像分类分析：一种面向水稻叶部病害预测的多功能应用及农户建议
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
AI总结：
本研究基于8种CNN算法构建水稻叶部病害分类系统，其中MobileNetV2准确率达95.83%，并集成Tkinter应用为农户提供实时预测与个性化建议。
摘要：
本研究提出了一种利用8种不同卷积神经网络（CNN）算法改进水稻病害分类的新方法，这将进一步推动精准农业领域的发展。基于Tkinter的应用程序为农户提供了功能丰富的界面。借助这一前沿应用，农户能够通过实时病害预测和个性化建议，做出及时且明智的决策。将ResNet-50、InceptionV3、VGG16和MobileNetv2等前沿CNN迁移学习算法技术与UCI数据集平滑集成，并结合用户友好的Tkinter界面，标志着农业实践现代化和保障可持续作物管理的重大进步。显著的实验结果包括：ResNet-50准确率为75%，DenseNet121准确率为90%，VGG16准确率为84%，MobileNetV2准确率为95.83%，DenseNet169准确率为91.61%，InceptionV3准确率为86%。这些结果简明概括了各模型的能力，有助于研究人员选择合适策略以实现精确且成功的水稻病害识别。VGG19出现了严重过拟合，准确率为70%；Nasnet准确率为80.02%。ResNet101仅达到54%的准确率，而EfficientNetB0仅达到33%。一个经MobileNetV2训练的模型已成功部署在Tkinter GUI应用程序上，可利用图像或实时视频捕获进行预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
