Computer Vision Based Solanum Lycopersicon Leaf Disease Detection Using Transfer Learning

Abstract

Solanum Lycopersicon, mostly known as tomatoes, are one of the most essential and extensively consumed crops, with yields varying depending on cultivation methods. Tomato leaf disease is the most critical factor in both supply and quality of tomato crops. As a result, it is essential to identify and determine these diseases appropriately. Different diseases can impact the production of tomatoes, and early detection is crucial in reducing the consequences and encouraging a healthy crop yield. Improved approaches for disease detection and classification have been widely used. Various studies have been proposed to identify tomato leaf diseases, but they must be enhanced due to their accuracy and effectiveness as trained on the limited dataset. This work aims to support farmers in accurately diagnosing early-stage tomato leaf diseases namely: Bacterial spot, leaf spot, blight, curl virus, leaf mold diseases and delivering necessary information. In this study, deep learning-based models applied using a transfer learning technique. Different models, such as ResNet-50, VGG-16, and VGG- 19 are applied. Python is used to train deep learning models using Google Colab. The dataset acquired from publicly available repositories, namely, Kaggle. The image data is preprocessed using resizing and rescaling. The applied models will be evaluated in accuracy and performance to choose the best one.