Tomato Classification

Tomato classification research focuses on automating the detection and categorization of tomatoes at various stages of ripeness and growth, primarily to improve efficiency and quality control in agriculture. Current efforts leverage computer vision techniques, employing deep learning models like YOLO variants and convolutional transformers, along with hyperspectral imaging and novel feature selection methods, to achieve high accuracy in both detection and maturity classification. These advancements offer significant potential for optimizing harvesting, grading, and disease detection, leading to increased yields and reduced labor costs in tomato production.