Freshness Grading of Tomato Using Fast R-CNN

The impacts of Artificial Intelligence (AI) and Computer Vision in the agricultural sector in today's high-world of imported technological solution is changing the way agriculture runs using numerical models. One such innovation is the development of systems able to evaluate the quality and freshness of agricultural product with high quality of information, e.g., the tomatoes. In this project we want to solve the problem of reliably grading the freshness of tomatoes using computer vision method. With deep learning and object detection power, we have used Fast R-CNN which improves upon traditional region-based CNN (R-CNN) by making the process faster and more efficient to recognize and categorize tomatoes depending on their freshness grade. The features of this model allow producers and distributors to determine the quality of their tomatoes efficiently, thus improving supply chain management and keeping consumers happy.