Detection and Classification of Plant Diseases in Crops (Solanum lycopersicum) due to Pests Using Deep Learning Techniques: A Review

Abstract

All people have access to food thanks to agriculture, even in areas with rapid population expansion. In order to ensure that the entire population has access to food, early diagnosis of plant diseases is recommended. However, when crops are still maturing, it might be challenging to predict diseases. The goal of this paper is to inform farmers about the use of machine learning to recognize various pest species in various crop plants. Deep learning establishes a continuing, cutting-edge method for analysing images with significant potential and promising outcomes. DL has expanded into the field of agriculture after demonstrating its effectiveness in a number of applications. Here, we reviewed various research articles that used deep learning methods to address diverse research issues in tomato (Solanum lycopersicum) plants. We look at the study areas in tomato plants where deep learning, data preprocessing, transfer learning, and augmentation approaches are performed. studied dataset details, including the number of photos, classes, and train test to validation ratios that were employed. We also look at comparisons made across different deep learning architectures and analyse the results. The results demonstrated that deep learning techniques beat all other image processing methods, however how well DL works depended on the dataset employed.