Development of weighted ensemble transfer learning for tomato leaf disease classification solving low resolution problems

Abstract

ABSTRACT The manual prediction of Tomato Leaf Diseases (TLD) is more time-consuming, and the implementation cost is high. The various natural characteristic and the low resolution of images make a difficult task for plant disease recognition. The main intention of this paper is to develop a novel tomato leaf disease classification using deep learning techniques, especially for solving the low-resolution issues in the images. The pre-processing is done by the contrast enhancement technique and then the low-resolution problem in pre-processed images is done through Deep Convolutional Neural Networks (DCNN). The segmentation of diseases is carried out through Mask Region-Based Convolutional Neural Networks (Mask R-CNN). The weight of each classifier score is optimized by Controlling Parameter-based Artificial Gorilla Troops Optimization (CP-AGTO). The accuracy and sensitivity of the recommended technique attain 92% and 93%. Thus, the efficacy of the proposed model has been examined with several performance metrics over various recent approaches.