CNN-based solution for mango classification in agricultural environments

• URL: http://arxiv.org/abs/2507.23174v1
• Date: Thu, 31 Jul 2025 00:58:34 GMT
• Title: CNN-based solution for mango classification in agricultural environments
• Authors: Beatriz Díaz Peón, Jorge Torres Gómez, Ariel Fajardo Márquez,
• Abstract summary: This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN)<n>The goal is to develop a system that automatically assesses fruit quality for farm inventory management.
• Score: 1.2248397169100782
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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