Non-Invasive Grading and Sorting of Mango (Mangifera indica L.) Using Antlion Optimizer-Based Artificial Neural Networks

Abstract

Mango is an imperative commercial fruit in terms of market value and volume of production. In addition, it is grown in more than ninety nations around the globe. Consequently, the demand for effective grading and sorting has increased, ever since. This communication describes a non-invasive mango fruit grading and sorting model that utilizes hybrid soft computing approach. Artificial neural networks (ANN), optimized with Antlion optimizer (ALO), are used as a classification tool. The quality of mangoes is evaluated according to four grading parameters: size (volume and morphology), maturity (ripe/unripe), defect (defective/healthy) and variety (cultivar). Besides, a comparison of proposed grading system with state-of-the-art models is performed. The system showed an overall classification rate of 95.8% and outperformed the other models. Results demonstrate the effectiveness of proposed model in fruit grading and sorting applications.