Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

Abstract

Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this study, the best wavelengths with which to discriminate plants affected by Lw from stress factors were determined and classified. Visible-near infrared (400-950 nm) spectral data from healthy trees and those with Lw, Phytophthora, or salinity damage were collected using a handheld spectroradiometer. The total number of wavelengths was averaged in two ranges: 10 nm and 40 nm. Three classification methods, stepwise discriminant (STEPDISC) analysis, multilayer perceptron (MLP), and radial basis function (RBF), were applied in the early stage of Lw infestation. The classification results obtained for MLP, with percent accuracy of classification as high as 98% were better than STEPDISC and RBF. The MLP neural network selected certain wavelengths that were crucial for correctly classifying healthy trees from those with stress trees. The results showed that there were sufficient spectral differences between laurel wilt, healthy trees, and trees that have other diseases; therefore, a remote sensing technique could diagnose Lw in the early stage of infestation.

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

“…However, the asymptomatic stage is a crucial stage for detecting a disease. Any delay on disease detection might affect a tree's health or even kill them in some situations [62][63][64]. The canker lesions on leaves in late disease development stage turns brown, the edges appear water-soaked, and it develops a yellow halo.…”

Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado

“…Techniques in hyperspectral data analysis have grown rapidly in recent years. Similar work is being achieved in the field of hyperspectral data analysis for plant disorders' detection and classification [6,7,11,[19][20][21][22][23][24][25][26][27][28][29][30]. Perez-Bueno et al [25] have used normalized difference vegetative index (NVDI) and canopy temperature to form a binary regression model for classification of hyperspectral data in determining white root rot in avocados.…”

“…Hyperspectral imaging techniques have been utilized recently to distinguish diseases in the asymptomatic and early stages of Lw disease in avocado. Abdulridha et al [6,7] and Sankaran et al [6,7,19] developed rapid techniques to detect Lw in avocado and distinguish it from other diseases and disorders, which produce similar symptoms, utilizing hyperspectral and multispectral data and several classification algorithms (neural networks). Two data sets were collected at 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.…”

Root-associated entomopathogenic fungi manipulate host plants to attract herbivorous insects

“…The red edge is well known as spectral region to respond to both abiotic and biotic stressors 20 . Several studies have demonstrated that inoculation of plants with pathogenic fungi cause a decrease in leaf reflectance in spectral bands between 900 and 1000 nm 21 , 22 . In general, average leaf reflectance in spectral bands from 760 to 2500 nm are known to be influenced by factors, such as, leaf structure, water content, surface roughness, and stoma activity 23 , 24 .…”