Enhancing anthracnose detection in mango at early stages using hyperspectral imaging and machine learning

Abstract

Anthracnose, caused by Colletotrichum sp. infections, poses a significant threat to mango production worldwide, resulting in substantial losses. This devastating disease is challenging to detect and control, primarily due to its ability to spread rapidly. The methods currently used to control anthracnose are primarily corrective, relying on disease detection in the late stages when the infection becomes visible. Hence, there is a need for tools to detect the infection at early stages, before symptoms appear. Hyperspectral imaging systems are promising for developing non-destructive solutions to assess and detect external and internal damage in fruit, including decay caused by anthracnose. These advanced imaging systems make early detection possible before the symptoms are visible, allowing for timely intervention and potentially more effective disease control. This work aims to evaluate the possibility of early detection of anthracnose in two mango cultivars using hyperspectral imaging and machine learning methods. Secondly, to establish correlations between specific wavelengths and the physicochemical symptoms associated with anthracnose. Lastly, to develop a robust model for the spectral detection of anthracnose on mango fruit. Mangoes were inoculated with spores of Colletotrichum gloeosporioides. Hyperspectral images of control and infected fruit were captured in the 450–970 nm spectral range. Five machine-learning models were used to obtain the method that best fits the spectral data. The best model achieved an accuracy = 0.961, recall = 0.961, specificity = 0.992, F1 = 0.961 and Matthews correlation coefficient (MCC) = 0.953 for 'Keitt', and an accuracy = 0.975, recall = 0.976, specificity = 0.995, F1 = 0.975 and MCC = 0.971 for 'Osteen', showing the feasibility to detect early anthracnose infection in mango fruit within 48 h after pathogen inoculation.