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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.23174 |
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| _version_ | 1866915418604568576 |
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| author | Peón, Beatriz Díaz Gómez, Jorge Torres Márquez, Ariel Fajardo |
| author_facet | Peón, Beatriz Díaz Gómez, Jorge Torres Márquez, Ariel Fajardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CNN-based solution for mango classification in agricultural environments Peón, Beatriz Díaz Gómez, Jorge Torres Márquez, Ariel Fajardo Computer Vision and Pattern Recognition Machine Learning 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. |
| title | CNN-based solution for mango classification in agricultural environments |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.23174 |