Development of a sorting system for mango fruit varieties using convolutional neural network

Abstract

Mango is a tropical fruit with numerous varieties, these varieties intermix during harvest and post-harvest procedures thereby causing complications and inability to accurately identify specific varieties at the retail stage. Accuracy of existing sorting techniques does not fit well to real-world scenarios. This research introduces an enhanced sorting system for mango fruits to address these challenges. Our approach involved building a comprehensive database by photographing six distinct mango fruit varieties prevalent in South-West Nigeria using a digital camera. The captured images underwent quality enhancement through histogram equalisation and noise reduction via median filtering. The convolutional neural network framework was used in the creation of a model named AdeNet to facilitate feature extraction and classification within the system. The experimental result achieved 99.0% accuracy and F1-score of 97.6% which is better than the performance of existing mango sorting techniques. The work will enhance the efficiency of mango industries.