Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm

Abstract

The aim of this paper is to explore the importance of leaf wilting status detection and classification in agriculture to meet the demand for monitoring and diagnosing plant growth conditions. By comparing the performance of the traditional VGG16 image classification algorithm and the popular EfficientNet V3 algorithm in leaf image wilting status detection and classification, it is found that EfficientNet V3 has faster convergence speed and higher accuracy. As the model training process proceeds, both algorithms show a trend of gradual convergence of Loss and Accuracy and increasing accuracy. The best training results show that VGG16 reaches a minimum loss of 0.288 and a maximum accuracy of 96% at the 19th epoch, while EfficientNet V3 reaches a minimum loss of 0.331 and a maximum accuracy of 97.5% at the 20th epoch. These findings reveal that EfficientNet V3 has a better performance in leaf wilting status detection, which provides a more accurate and efficient means of plant health monitoring for agricultural production and is of great research significance.