Work

Automated Physical Feature Extraction of Namdokmai Sithong Mangoes Using YOLOv8 and Image Processing Techniques

Year: 2025

Type: article

Abstract: Accurate and consistent measurements of geometric features such as fruit length and width are essential for the quality assessment of Namdokmai Sithong mangoes. Traditional manual methods are labor-intensive and prone to inconsistency. This study presented an automated system for geometric feature extraction of Namdokmai Sithong mangoes using a YOLOv8-based object detection model. The framework automated the process of measuring key morphological traits, specifically fruit length and width, to improve accuracy and consistency in quality assessment. The model identified an anatomically meaningful reference point for guiding axis-based measurements by detecting the mango and its peduncle. HSV-based image segmentation combined with morphological operations and edge detection effectively calculated the major (length) and minor (top and bottom width) axes of the fruit. Evaluation on 30 test images showed that the proposed method achieved error rates below 5% in over 90% of samples, with average deviations for fruit length typically under 1.5%. The system was implemented as a standalone Python (version 3.12.8) application and demonstrated high potential for use in real-time, automated fruit grading scenarios.

Source: AgriEngineering

DOI: 10.3390/agriengineering7090312

Authors: Sujitra Arwatchananukul, Suphapol Wongsawat, Saowapa Chaiwong, Min Chen, Rattapon Saengrayap

Institutions: Mae Fah Luang University, Hunan Institute of Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,930