Performance Results of Plant Disease Classification Pretrained Models

This dataset contains the performance results of five pre-trained deep learning models—EfficientNetV2B0, ResNet50, InceptionV3, DenseNet121, and VGG16—evaluated for plant disease classification tasks. The dataset includes metrics from experiments conducted on both training and testing datasets, focusing on key performance indicators such as accuracy, precision, recall, F1-score, and confusion matrices. The aim is to provide a comprehensive comparison of these models' capabilities for detecting and classifying plant diseases.

本数据集收录了5款预训练深度学习模型——EfficientNetV2B0、ResNet50、InceptionV3、DenseNet121及VGG16——在植物病害分类任务中的性能评估结果。该数据集涵盖了在训练集与测试集上开展的实验所得的各项指标，重点关注准确率（accuracy）、精确率（precision）、召回率（recall）、F1分数（F1-score）与混淆矩阵（confusion matrix）等核心性能指标，旨在全面对比上述模型在植物病害检测与分类任务中的性能表现。