Title: Comparison of convolutional neural networks architectures for mango leaf classification

Authors: B. Jayanthi; Lakshmi Sutha Kumar

Addresses: Department of Electronics and Communication, National Institute of Technology Puducherry, Karaikal, India ' Department of Electronics and Communication, National Institute of Technology Puducherry, Karaikal, India

Abstract: Plant diseases are a threat to the food supply as they reduce the yield, and reduce the quality of fruits and grains. Hence, early identification and classification of plant diseases are essential. This paper aims to classify mango plant leaves into healthy and diseased using convolutional neural networks (CNNs). The performance comparison of CNN architectures, AlexNet, VGG-16 and ResNet-50 for mango plant disease classification is provided. These models are trained using the Mendeley dataset, validation accuracies are found and compared with and without the use of transfer learning models. AlexNet (25 layers, 6.2 million parameters) produces a testing accuracy of 94.54% and consumes less training time. ResNet-50 (117 layers, 23 million parameters) and VGG-16 (16 layers, 138 million parameters) have given testing accuracies of 98.56% and 98.26% respectively. Therefore, based on the accuracies achieved and complexity, this paper recommends AlexNet followed by ResNet-50 and VGG-16 for plant leaf disease classification.

Keywords: convolution neural networks; neural network; image classification; precision agriculture.

DOI: 10.1504/IJCVR.2024.135131

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.1, pp.84 - 98

Received: 21 Feb 2022
Accepted: 05 Jul 2022
Published online: 01 Dec 2023 *