An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
• Main Authors: Pandian J., Arun, K., Kanchanadevi, Rajalakshmi, N.R., G.Arulkumaran
• Format: Journal Article
• Language: English
• Published: New York Hindawi 14.09.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Affine transformations; Artificial intelligence; Artificial neural networks; Crop diseases; Datasets; Evolution; Genetic transformation; Graphics processing units; Leaves; Medical imaging; Neural networks; Plant diseases; Plants; Saturation (color); Transfer learning
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/5102290

In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.