Skill-Honey Badger Optimisation Algorithm-Enabled Deep Convolutional Neural Network for Multiclass Leaf Disease Detection in Tomato Plant

Abstract

In today's life, agriculture holds considerable importance in human life and the economy of a nation. Agriculture, including tomato farming, plays a vital role as one of the most extensively consumed vegetables worldwide. However, tomato crops are very prone to diseases, leading to reduced production and economic down in agricultural fields. To solve these issues, an effective method is proposed named Skill-Honey Badger Optimisation Algorithm-enabled deep convolutional neural network (CNN) (SHBOA_DeepCNN) for detecting leaf disease in tomato plants. In this method, the input is primarily preprocessed by utilising Savitzky–Golay (SG) filtering. Then, segmentation is performed by utilising Dense-Res-Inception Net (DRINet), which is trained by using devised SHBOA. The proposed SHBOA is designed by incorporating the Skill Optimisation Algorithm (SOA) and Honey Badger Algorithm (HBA). Subsequently, image augmentation is performed on segmented images by using two augmentation techniques, namely, colour augmentation and position augmentation. At last, multiclass leaf disease detection is performed using DeepCNN, which is trained by devised SHBOA. The experimental analysis of the devised SHBOA_DeepCNN method showed a high accuracy of 91.91% and a true positive rate (TPR) of 90.24%. Moreover, it achieved a minimum false positive rate (FPR) of 7.38%. The code of the article is available at “https://github.com/Amisra-98/SHBOA_DeepCNN.git”.