Classification of Plant Leaf Diseases Based on EfficientNet

With the in-depth research of deep learning technology, convolutional neural networks have been widely applied in the field of image recognition. However, traditional diagnostic methods for plant leaf diseases often rely on expert experience and time-consuming manual detection, which is inefficient and limited in accuracy. Based on the above background, this paper mainly studies the plant leaf disease classification method based on EfficientNet. Firstly, the EfficientNet algorithm is reviewed, followed by a detailed description of the process of the plant leaf disease classification algorithm and then an analysis of the dataset used. In the simulation analysis, this paper explores the impact of different parameters on the performance of EfficientNet and demonstrates its effectiveness in plant leaf disease classification. The proposed method has efficient and accurate results, significant research significance, and application prospects in the fields of agricultural automation and intelligent diagnosis.