A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination

Abstract

The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.