Deep Convolutional Neural Network Approach for Tomato Leaf Disease Classification

Plant disease detection and classification are required to identify the early symptoms of disease in plants and crops to avoid their mitigation to entire croplands. Deep learning is rapidly getting involved in plant disease identification because of the large amount of image data which is otherwise difficult to learn. The paper has performed the tomato leaf disease classification based on convolutional neural network along with transfer learning where the data is collected from the PlantVillage dataset. The dataset comprises 10 classes containing nearly 16,000 leaf images. The data is divided into 70%, 20%, and 10% ratios into training, test, and validation set, respectively. The paper has shown a CNN model developed from scratch for learning, whose results have been compared with four different transfer learning models: DenseNet121, ResNet50, Inception-V3, and VGG-16. The models are evaluated using accuracy and cross-entropy loss. VGG-16 and CNN model developed from scratch has shown promising results on the given dataset with 90% and 83% validation accuracy, respectively, on the test set.