Detection of White Root Rot in Avocado Trees by Remote Sensing.

Abstract

White root rot, caused by the soilborne fungus Rosellinia necatrix , is an important constraint to production for a wide range of woody crop plants such as avocado trees. The current methods of detection of white root rot are based on microbial and molecular techniques, and their application at orchard scale is limited. In this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods. Normalized difference vegetation index (NDVI) and normalized canopy temperature (canopy temperature - air temperature) were tested as predictors of disease by several algorithms. Among them, logistic regression analysis (LRA) trained on NDVI data showed the highest sensitivity and lowest rate of false negatives. This algorithm based on NDVI could be a quick and feasible method to detect trees potentially affected by white root rot in avocado orchards.

Plant phenotyping relevance

アボカド樹の病害状態を、画像由来のNDVI・樹冠温度と機械学習で推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper describes NDVI/thermal UAV data collection and classifier analysis (ANN, LRA, LDA, SVM in SPSS) but contains no public deposit of the phenotype datasets, imagery, trained models, or author analysis code. The only URL in the text (MAPAMA statistics) is a cited external reference, not a paper-specific asset. A

No evidence-backed public reproduction asset is currently recorded.