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

Authors: Philip Oluwaseun Adejumobi; John Adedapo Ojo; Israel Oluwamayowa Adejumobi; Oluwadare Adepeju Adebisi; Samson Oladayo Ayanlade

Addresses: Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria ' Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria ' Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria ' Department of Mechanical and Mechatronics Engineering, First Technical University, Ibadan, Nigeria ' Department of Electrical and Electronic Engineering, Lead City University, Ibadan, Nigeria

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.

Keywords: artificial intelligence; convolutional neural network; CNN; deep learning; machine learning; mango fruit; automatic sorting system.

DOI: 10.1504/IJCSE.2025.143466

International Journal of Computational Science and Engineering, 2025 Vol.28 No.1, pp.87 - 99

Received: 10 Aug 2023
Accepted: 31 Jan 2024
Published online: 21 Dec 2024 *

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