Prediction of Tomato Leaves Disease Using Ensemble Learning Algorithms

Abstract

Tomato is an important vegetable crop worldwide, and its production is threatened by various diseases. Major declines in crop yield and quality can be prevented by early diagnosis and detection of these diseases. Deep learning techniques have shown promising results in automatic detection of tomato leaf diseases. This survey paper presents a comprehensive overview of recent research on tomato leaf disease detection using deep learning. We summarize the datasets, architectures, and evaluation metrics used in the literature, and also find out some important challenges in upcoming days. But because of different leaf diseases as mosaic virus, bacterial spot, late blight, yellow leaf curl virus, etc., the quality and yield of tomato crops decline. Therefore, we are suggesting a deep learning-based system employing Resnet 152v2 and MobileNet v2 to detect the disease in tomato leaves, which makes use of many techniques to attain a decent crop production.