Machine vision based automatic sorting of cherry tomatoes

Tomatoes will be harvested at the earlier stage for longer market life. Therefore while grading tomatoes, maturity as well the quality need to be considered. In this paper automatic and non destructive grading of cherry tomatoes based on maturity and quality has been proposed. This algorithm comprises two phases of grading. First phase grades tomatoes in terms of maturity and the second phase grades matured tomatoes in terms of quality. Using the concept of change in external color during different maturity stages, a color based maturity estimation algorithm has been proposed. And to classify the tomato in the second phase, texture, color and shape features correlated to external and internal characteristics are extracted from the surface of tomato. To extract the tomato surface alone from the background and leaves a new color based segmentation based on Euclidean distance has also been proposed. Then the extracted features are given to K-Nearest Neighbor based Support Vector Machine classifier to classify the matured fruit into three classes Class I, II and III. This classifier outperforms both SVM and KNN classifier in terms of accuracy and computation time.