Transfer Learning-Based EfficientNetB0 Model for Multi-Crop Plant Disease Classification

globally. Early and accurate diagnosis of diseases is key to reducing losses and improving agricultural practices. Traditional methods of plant disease diagnosis are known for being lengthy, cumbersome, and prone to human error. Recent developments in deep learning have shown promise in the automatic diagnosis and classification of plant diseases. Namely, convolutional neural networks (CNNs) perform quite well for such problems. The purpose of this study is to present an approach to plant disease classification that is based on the use of transfer learning and utilizes the Convolutional Neural Network architecture called EfficientNetB0. Transfer learning with ImageNet pretrained weights and an efficient architecture for feature extraction was chosen because of its ability to significantly lower the computational resources required and improve performance. Moreover, plant leaf images from different datasets were taken into account to provide better training. The model achieved 99.32 % training accuracy, 99.37% validation accuracy, and 99.32% test accuracy. It also had impressive convergence capability and low computational complexity, with around 4.7 million parameters and the size of the saved model at around 34 MB. It can be concluded from the findings that the transfer learning-based EfficientNetB0 model provides a great balance between accurate classification and efficiency, while improving the practicality of the system.