Papaya Ripeness Prediction Using Machine Learning

Abstract

Fruits are a rich source of energy, minerals, and vitamins. Papaya is a perennial fruit of commercial importance due to its high nutritional properties. [13] The packaging of papaya fruit as per its maturity status is an essential task in the fruit industry. Classification of RIPE fruit can be used in many applications, whether industrial, agriculture or services, for example, it can help the manager in the hyper mall to determine the price according to the maturity(days). Manual labeling of papaya fruit based on human visual perception is time-consuming and inaccurate. [13] In this paper, a machine learning-based approach is presented for classifying and identifying 7 different labels (days) with a dataset that contains 139 images, divided into 4 batches. Each batch contains 35 images. Use 28 images for training, 3 images for validation, and 4 images for testing. Few deep learning models were used that were extensively applied to image recognition. We used 80% of the images for training and 10 % of the images for validation and 10% for testing. One of our trained models achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach.