SLSU Researchers Publish Breakthrough Study on KNN-Based Cacao Grading System for Enhanced Quality Control

Cacao, the essential ingredient for chocolate and cocoa products, is a crucial crop in the tropics. Ensuring the quality of cacao beans through grading is a vital step for farmers and suppliers, but traditional manual grading methods are often laborious and less accurate. In response, faculty researchers from Southern Leyte State University’s College of Computer Studies and Information Technology—Jannie Fleur V. Oraño, Francis Rey F. Padao, and Rhoderick D. Malangsa—have developed a KNN-based cacao bean grading system that automates this process.

Their system, created with image processing and K-Nearest Neighbors (KNN) algorithm, was tested with 190 training samples and 60 classification samples, achieving an impressive 93.33% accuracy. Built on C# with XAMPP and MySQL for database management, this AI-driven approach offers an efficient, reliable solution for cacao grading, promising better quality control and support for the cacao industry. This brings substantial benefits to the cacao industry, where demand for high-quality beans continues to grow. By improving grading accuracy and consistency, this approach supports better quality control across the supply chain, helping ensure that only top-quality beans reach the market. This technological advancement represents a step forward in agricultural innovation, as it demonstrates how deep learning can optimize processes and enhance productivity in traditional industries like cacao production.

This article aligns with SDG 9: Industry, Innovation, and Infrastructure
To read full content:
 https://ieeexplore.ieee.org/document/9072790