Detection of aphid infestation on faba bean (Vicia faba L.) by hyperspectral imaging and spectral information divergence methods

Aphids hide under leaves, reproduce rapidly, and require early detection to prevent crop damage, disease transmission, and ensure effective pest management. This study presents a novel approach for aphid detection by utilizing hyperspectral imaging, multivariate classification methods and spectral information divergence (SID) analyses. The hyperspectral images average spectrum (n = 336) showed significant differences between healthy and infested leaves. Time-series classification was performed over 14 days after infestation using four distinct machine learning algorithms. Early-stage infection detection may not relate to internal physiological alterations within the leaf but rather to the physical presence of the aphid behind the leaf, obstructing subtle physiological signatures. Implementation of spectral endmembers in the VIS–NIR reference spectrum led to the identification of an informative abundance SID map within the 710–825 nm range, useful for further classification. Machine learning classification resulted in support vector machines achieving 99.20 accuracy. Using random forest, twenty-two most important variables found effective in boosting classifier performance. The selected model also extended to real-world scenarios by testing progressing infestation patterns over 14 days on independent data sets, confirming the system’s reliability. Signal normal variant pre-treatment with partial least squares regression was effective in the estimation of aphid populations, achieving a 0.81 coefficient of determination (R2) and a 10.29 root-mean-square error of prediction for test datasets. In conclusion, the proposed method was able to successfully detect aphid colony infestation, both earlier and in locations that are invisible during standard human inspection.