Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado

Laurel wilt (Lw) disease is an exotic and lethal disease that can kill laurel family trees very fast. It is vectored by the redbay ambrosia beetle that prefers to live and lay eggs inside avocado trees (among other plants). Lw disease continues to expand in Florida posing a major threat to the avocado industry. Early and accurate disease detection is very critical in this case to remove infected trees and distinguish Lw disease from other diseases or disorders with similar symptoms. Herein, we present a nondestructive remote sensing method to detect Lwinfected avocado trees (in early and late stage) and discriminate them from healthy and other factors that cause similar symptoms, such as iron and nitrogen deficiencies, by using a portable spectral data collection system (visible - near infrared; 400-970 nm). Two data sets were collected in 10 nm and 40 nm spectral resolution, and 23 vegetation indices (VIs) were calculated to detect Lw-affected trees by using two classification methods: decision tree (DT) and multilayer perceptron (MLP) neural networks. Additionally, the optimal wavelengths and VIs to discriminate healthy, Lw-infected and avocado trees with iron and nitrogen deficiencies were identified. The results showed that it was possible to detect Lw-infected trees at early stage and distinguish them from other biotic and abiotic factors with high accuracy (around 100%) using the MLP method. Poorer results were achieved with DTs. The optimum 10 nm wide bands and VIs selected for the Lw-detection were found in the red, red-edge and NIR bands.