Identification of plant leaf diseases using a nine-layer deep convolutional neural network

• Published in: Computers & electrical engineering Vol. 76; pp. 323 - 338
• Main Authors: G, Geetharamani, J, Arun Pandian
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.06.2019; Elsevier BV
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Computer simulation; Data augmentation; Deep convolutional neural networks; Deep learning; Dropout; Image augmentation; Leaf diseases identification; Machine learning; Medical imaging; Mini batch; Model accuracy; Model testing; Neural networks; Plant diseases; Principal components analysis; Training epoch; Transfer learning; Deep convolutional neural networks; Deep learning; Transfer learning; Machine learning; Mini batch; Image augmentation; Leaf diseases identification; Artificial intelligence; Training epoch; Dropout
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2019.04.011

•We proposed a 9-layer deep convolutional neural network for leaf disease identification.•We used six different types of image augmentation methods, and compared their validation performances using data augmentation and not using data augmentation.•Fine-tuning the batch size, dropout and epoch improves the performance of our model.•We validated that the proposed model performs better than transfer learning techniques.•Our model produced an average testing accuracy of 96.46%, which was better than the state-of-the-art machine learning approaches. In this paper, we proposed a novel plant leaf disease identification model based on a deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 39 different classes of plant leaves and background images. Six types of data augmentation methods were used: image flipping, gamma correction, noise injection, principal component analysis (PCA) colour augmentation, rotation, and scaling. We observed that using data augmentation can increase the performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. Compared with popular transfer learning approaches, the proposed model achieves better performance when using the validation data. After an extensive simulation, the proposed model achieves 96.46% classification accuracy. This accuracy of the proposed work is greater than the accuracy of traditional machine learning approaches. The proposed model is also tested with respect to its consistency and reliability.