Computer Vision-Based Tomato Grading and Sorting

Since ages, agricultural sector plays an important role in the economic development of a country. In recent years, industries have started using automated systems instead of manual techniques for quality evaluation. In agriculture field, grading is very necessary to increase the productivity of the vegetable products. Everyday a huge amount of vegetables are exported to other places and earn a good profit. So, quality evaluation is important in terms of improving the quality of vegetables and gaining profit. Traditionally, the vegetable grading and classification were done through manual procedures which were error prone and costly. Computer vision-based systems provide us such accurate and reliable results that are not possible with human graders/experts. This paper presents a vegetable grading and sorting system based on computer vision and image processing. For this work, tomatoes have been used as a sample vegetable. A total of 53 images were acquired using own camera setup. Afterward, segmentation using Otsu's method was performed so as to separate the vegetable from the background. The segmented images, thus obtained, were used to extract color and shape features. At last, grading and sorting were performed using backpropagation neural network. The proposed method has shown an accuracy of 92% and outperformed the existing system.