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In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques. The DCNN model was trained on an augmented dataset of over 240,000 images of different healthy and diseased plant leaves and backgrounds. Five image augmentation techniques were used: Generative Adversarial Network, Neural Style Transfer, Principal Component Analysis, Color Augmentation, and Position Augmentation. The random search technique was used to optimize the hyperparameters of the proposed DCNN model. This research shows the significance of choosing a suitable number of layers and filters in DCNN development. Moreover, the experimental outcomes illustrate the importance of data augmentation techniques and hyperparameter optimization techniques. The performance of the proposed DCNN was calculated using different performance metrics such as classification accuracy, precision, recall, and F1-Score. The experimental results show that the proposed DCNN model achieves an average classification accuracy of 98.41% on the test dataset. Moreover, the overall performance of the proposed DCNN model was better than that of advanced transfer learning and machine learning techniques. The proposed DCNN model is useful in the identification of plant leaf diseases.

A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection