CNN-based solution for mango classification in agricultural environments
This is an incremental improvement for agricultural quality control, applying existing methods to a specific domain.
The paper tackled automated mango fruit classification for farm inventory management using a CNN-based system with ResNet-18 and a cascade detector, achieving a reliable solution with balanced accuracy and efficiency.
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.