CNN-based solution for mango classification in agricultural environments

TL;DR

This paper presents a CNN-based system utilizing Resnet-18 and cascade detectors for accurate and efficient mango fruit detection and classification in agricultural settings, integrated with a user-friendly graphical interface.

Contribution

It introduces a novel combination of CNN and cascade detection tailored for mango classification, optimized for agricultural use.

Findings

High accuracy in mango detection and classification

Efficient processing suitable for real-time applications

Effective integration with a graphical interface

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.