Classification of Tomato from Coace to Vrot Using Machine Learning Techniques

Kalaivani Rajamoorthi, Alagar. S. Tarun, Vigneshwaran. M, Kavya. S and Saravanaprasath S
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Kalaivani Rajamoorthi: Student, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, India.
Alagar. S. Tarun: Student, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, India.
Vigneshwaran. M: Student, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, India.
Kavya. S: Student, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, India.
Saravanaprasath S: Student, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, India.

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 7, 1259-1269

Abstract: Fruit distribution and sales depend heavily on maximizing profits and ensuring customer satisfaction, which emphasizes the significance of determining the fruits’ quality. There are differences in the ripeness stages of tomatoes, and it can be difficult to spot spoiled or unripe ones in a big batch. Significant financial losses may arise from a single spoiled tomato in a batch hastening the spoilage of the entire lot. To solve this problem, a clever system that categorizes tomatoes according to their freshness has been created using image processing technology. With precision, the system classifies tomatoes into ripe, semi-ripe, unripe, and rotten categories by examining visual cues that indicate ripeness and spoilage. By using machine learning techniques, the sorting and grading process is streamlined as the system becomes adept at identifying patterns in tomato images.

Date: 2025
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