On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves

Maize is one of the world's most important food crops, but its cultivation is hampered by diseases. Rapid disease identification remains a challenge due to a lack of the necessary infrastructure. This necessitates the development of automated methods to identify diseases. In this research, the use of deep learning models to identify maize leaf diseases is proposed. In this article, we investigate the transfer learning of deep convolutional neural networks for the detection of maize leaf diseases and explore employing the knowledge of pre-trained models and then transferring the knowledge to our dataset. In this attempt, we employ pre-trained VGG16, ResNet50, InceptionV3, and Xception models to classify three common maize leaf diseases using a dataset of 18,888 images of healthy and diseased leaves. Besides, Bayesian optimization is used to choose optimal values for hyperparameters, and image augmentation is used to improve the model's ability to generalize. The work includes a comparative study and analysis of the proposed models. The results demonstrate that all trained models have an accuracy of more than 93% in classifying maize leaf diseases. In particular, VGG16, InceptionV3, and Xception achieved an accuracy of more than 99%. Furthermore, our methodology provides new avenues for the detection of maize leaf diseases.