Banana Ripeness Classification Based On Image Processing With Machine Learning

Abstract

Banana is one of the most consumed fruits globally. It contributes about 16% of the world’s fruit production according to FAO. Maturity stage of fresh banana fruit is a principal factor that affects the fruit quality during ripening and marketability after ripening. The machine learning techniques with adequate concepts of image processing have a great scope to provide intelligence for designing an automation system to differentiate the fruits according to its type, variety, matureness and intactness. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. In this paper, an automatic system is implemented to identify the ripening stages of banana from images. The feature extraction is performed using pre-trained deep convolution neural network i.e. Inception V3 to get the low to high level features automatically and later classification is carried out using various support vector machine learning algorithm to get ripening stages of fruit as predicted output.