Early diagnosis of cassava frog skin disease in powdered tissue samples using near-infrared spectroscopy

Abstract

Cassava frog skin disease (CFSD) causes significant yield losses in cassava (Manihot esculenta Crantz). One issue with control is late diagnosis. The objective of this work was to test near-infrared spectrometry (NIRS) and compare calibration and classification models for early detection of CFSD. NIRS analysis was performed on 238 cassava accessions (120 healthy and 118 with CFSD). Six classification models were used: a support vector machine with a linear kernel (SVM), a Bayesian generalized linear model (BGLM), a parallel random forest (PRANDF), an extreme learning machine (ELM), a high dimensional discriminant analysis (HDDA), and a partial least squares (PLS) model. Their predictive abilities were evaluated based on their accuracy and agreement (based on Cohen’s Kappa coefficient). The models demonstrated high efficiency at distinguishing diseased and healthy accessions, with an overall accuracy >80%. The Cohen’s Kappa coefficient values (0.83–0.98) for four of the models (the SVM, BGLM, PRANDF and PLS) indicated that there was almost perfect agreement between their classification results. The SVM and BGLM models exhibited high accuracy (99.07 and 98.92%, respectively) and reproducibility (both 0.98) in classifying the accessions according to their CFSD status. NIRS is a viable alternative for the detection of CFSD with the advantages of early and accurate detection, high speed and low cost compared to traditional diagnostic methods.