Biology

Identification (biology)

Materials science

Layer (electronics)

Computer science

Botany

Nanotechnology

Pattern recognition (psychology)

Artificial intelligence

Deep learning

Artificial neural network

Convolutional neural network

Computers & Electrical Engineering

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.

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