CNN-BASED SOLUTION FOR MANGO CLASSIFICATION IN AGRICULTURAL ENVIRONMENTS

Authors

  • Beatriz Díaz Peón Havana University of Technology ``"José Antonio Echeverría" (CUJAE)
  • Jorge Torres Gómez TU Berlin
  • Ariel Fajardo Márquez Havana University of Technology ``"José Antonio Echeverría" (CUJAE)

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.

INDEX TERMS: convolutional neural networks, MatLab, ResNet-18, cascade detector, regression with convolutional neural networks.

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Published

2025-09-06

How to Cite

Díaz Peón , B., Torres Gómez, J., & Fajardo Márquez, A. (2025). CNN-BASED SOLUTION FOR MANGO CLASSIFICATION IN AGRICULTURAL ENVIRONMENTS. Telemática, 23, 131–146. Retrieved from https://revistatelematica.cujae.edu.cu/index.php/tele/article/view/1010

Issue

Vol. 23 (2024): Vol. 23 (2024)

Section

Papers

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