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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.

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