A Novel Res + LSTM Classifier-Based Tomato Plant Leaf Disease Detection Model with Artificial Bee Colony Algorithm

World economy is mainly depending on agriculture. In recent days, many of the plants have been affected by various types of diseases, which will affect the economic growth of the country. Early detection of plant diseases may help to increase the profit rate for farmers. Tomatoes are the most consumable vegetable around the world. Catastrophic influence on food production safety is caused by plant diseases, and it decreases the quantum and eminence of agricultural products. The tomato plants are affected with different types of leaf spot diseases like grey spot, mosaic, leaf spot, brown spot, Alternaria, and also the rust and pests that may affect the growth of tomato plants at their various growth stages. Hence, the identification of leaf spot diseases that occur in the tomato plant is important to increase the survival rate of the tomato plants. Manual inspections of tomato leaf diseases are secured more time, and it mainly depends on the expert’s knowledge. To address this problem, a novel hybrid deep learning technique is developed for the detection of leaf spots in the tomato plant. The tomato leaf images with normal and affected tomato leaves are collected from real world and traditional benchmark datasets. After the tomato leaves are segmented using the U-net model, the resultant segmented images are subject to the classification phase. Finally, leaf spots in tomato leaves are classified with the help of residual network and long short-term memory (ResNet-LSTM), and the parameter in the Resnet and LSTM is tuned by utilizing artificial bee colony (ABC) algorithm to offer effective leaf spot classification rate. Finally, the severity level is computed. The experimental results demonstrate the effectiveness of the proposed model in detecting the leaf spot in the tomato plant.