Pathological, biochemical and molecular variability of Colletotrichum truncatum incitant of anthracnose disease in chilli (Capsicum annuum L.)

Hyperspectral imaging coupled with machine learning for classification of anthracnose infection on mango fruit

Anthracnose is the major plant disease causing an economic loss of mango fruit. Anthracnose symptom is not visible at a quiescent stage and the infected fruit often enters the food chain before the infection is known. Detection of a pre-symptomatic anthracnose infection is thus, crucial to prevent the infected fruit from entering the food chain. This research applied hyperspectral imaging (HSI) spectroscopy integrated with machine learning (ML) including principal component analysis (PCA) and support vector machine (SVM) for rapid identification of quiescent infection of anthracnose in mango fruit. Mango fruit (Nam Dok Mai Si Thong) was artificially infected with Colletotrichum gloeosporioides and stored at 20 °C and 90 % RH. The HSI was used to collect the spectral and spatial data of the samples. PCA and SVM were respectively performed to explore the hyperspectral data and to classify different symptom severities. The obtained spectral data can be recognized as fingerprints ascribing to the metabolites produced by C. gloeosporioides and the decomposed fruit tissues caused by the fungal infection. The HSI integrated with ML was able to not only detect the anthracnose infection at a latent stage before the onset of disease symptoms but also correctly classify different symptom severities. The symptom maps were also constructed using false-color image processing to simplify the data visualization of different symptom severities. The capability of detecting a pre-symptomatic anthracnose infection is a key advantage of the developed ML-assisted HSI.