A Hybrid Learning Model for Tomato Plant Disease Detection using Deep Reinforcement Learning with Transfer Learning

Abstract

Plant diseases play a significant role in damaging crop production and food security. Detecting and diagnosing plant diseases in the early stages obtains better management of diseases. Many plants are affected by these diseases, which are very dangerous for crop yield. This paper introduces advanced plant disease detection using an advanced preprocessing technique and a Hybrid Learning Model (HLM). The advanced preprocessing method uses a digital camera to capture high-resolution images of plant leaves from multiple angles. These images are then processed using an enhancement algorithm to improve the visual quality and clarity. The preprocessed images are subjected to a HLM model, which utilizes Deep Reinforcement Learning with Transfer Learning (DRL-TL). The DRL-TL architecture is designed to extract features from the preprocessed images in a three-dimensional manner, considering the spatial information of the leaves. It enables the model to capture precise patterns and variations indicative of disease symptoms. The pre-trained model MobileNetV2 trained on a tomato disease dataset belongs to labeled images; it consists of standard and affected plant leaves to learn the discriminative features associated with different diseases. Results obtained the effectiveness of the hybrid learning model. The preprocessing technique significantly increases the input images’ quality, enhancing the subsequent HLM’s performance. The model accurately identifies and classifies various plant diseases, outperforming existing methods. Furthermore, the hybrid learning model shows robustness and abstraction, successfully detecting diseases across plant species and environmental conditions.