Title: Improved performance on tomato pest classification via transfer learning-based deep convolutional neural network with regularisation techniques

Authors: Gayatri Pattnaik; Vimal K. Shrivastava; K. Parvathi

Addresses: School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India ' School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India ' Chaitanya Engineering College, Visakhapatnam, India

Abstract: Insect pests are major threat to the quality and quantity of crop yield. Hence, early detection of pests using a fast, reliable and non-chemical method is essential to control the infestations. Hence, we have focused on tomato pest classification using pre-trained deep convolutional neural network (CNN) in this paper. Four models (VGG16, DenseNet121, DenseNet169 and Xception) were explored with transfer learning approach. In addition, we have adopted two regularisation techniques viz. early stopping and data augmentation to prevent the model from overfitting and improve its generalisation ability. Among four models, the DenseNet169 achieved highest classification accuracy of 95.23%. The promising result shows that the DenseNet169 model with transfer learning and regularisation techniques can be used in agricultural pest management.

Keywords: agriculture; convolutional neural network; CNN; data augmentation; early stopping; pest; regularisation; tomato.

DOI: 10.1504/IJCSE.2023.132143

International Journal of Computational Science and Engineering, 2023 Vol.26 No.4, pp.397 - 405

Received: 01 Nov 2021
Accepted: 02 Mar 2022
Published online: 12 Jul 2023 *

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