CNN-based solution for mango classification in agricultural environments

2025/07/31 by Beatriz Díaz Peón, Peón, Beatriz Díaz, Jorge Torres Gómez +3
Agricultural and Biological Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Agriculture and AI

paper · pdf · doi:10.48550/arxiv.2507.23174

openalex publication_date 2025/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN). The goal is to develop a system that automatically assesses fruit quality for farm inventory management. Specifically, a method for mango fruit classification was developed using image processing, ensuring both accuracy and efficiency. Resnet-18 was selected as the preliminary architecture for classification, while a cascade detector was used for detection, balancing execution speed and computational resource consumption. Detection and classification results were displayed through a graphical interface developed in MatLab App Designer, streamlining system interaction. The integration of convolutional neural networks and cascade detectors proffers a reliable solution for fruit classification and detection, with potential applications in agricultural quality control.

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