Tomato TriSort: Smart Sorting System for Size, Color, and Pest Detection

The agricultural sector is a basic component of the national economy. The tomatoes add nutritional value to dietary items. India ranks 2nd in tomato production globally, but there is still a gap between demand and supply due to population growth. Traditional methods of grading tomatoes involve manual sorting by labor; manual sorting is expensive, with rising costs daily, and has the risk of inefficiencies, errors, etc. So, to meet the challenges, we propose a tomato sorting system that uses advanced technologies like machine learning and automation to increase accuracy and efficiency. The proposed system uses three main techniques: color sorting, size sorting, and pest detection. Color sorting for differentiating between ripe and unripe tomatoes. Pest detection uses deep learning and image processing to check the surface to detect the health of tomatoes. Size sorting to ensure uniformity. Automation reduces reliance on labor, minimizes human error, and post-harvest losses, and boosts the quality of tomatoes. The ability to grade tomatoes based on size, ripeness, color, and quality using a single system makes it unique over existing manual and flexible systems. The system is market-ready, supporting both farmers as well as traders. The system is scalable, manageable, and easy to use; moreover, it is more sustainable and profitable for agricultural practice. It is crucial in meeting the increasing demand for tomatoes and achieving the title of modern agriculture.