New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis

Abstract

AbstractAnthracnose is one of the most relevant diseases of mango crops in producing regions, affecting 60% of production. Currently, its detection is carried out in late stages by human visual inspection. Hyperspectral imaging systems allow the development of non-destructive solutions to inspect and detect internal damage. This work aimed to develop a system for detecting anthracnose in mango fruits using Vis–NIR hyperspectral imaging and discriminant analysis. The usefulness of three-dimensionality reduction methods to minimise redundancy in the spectral data and to obtain a compact number of wavelengths that effectively allow the detection of anthracnose symptoms in mango fruits is also explored. As a result, a classification model based on discriminant analysis and Pearson correlation coefficient was obtained, showing the potential of hyperspectral data to robustly allow the detection of anthracnose symptoms with full or reduced spectra. The findings reported in this study can serve as the basis for developing an anthracnose detection system in mango fruits with multispectral cameras.