Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach

Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, k-mean, naïve Bayes, support vector machine, feed-forward artificial neural network (FANN), all of were aimed at classifying ripeness mangoes harvest. The ML classifiers on biochemical data then tested physical electrical data.The performance models was compared fourfold cross validation. FANN classifier performed best, with a mean accuracy 89.6% for unripe, ripe, overripe classes, when other classifiers.