Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance.

Armando Sterling,Julio A Di Rienzo

doi:10.3390/plants11030329

Abstract

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO2 assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R2 ranged from 0.97 to 0.99) and testing (R2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R2 ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.

Highlights

IntroductionLatin America only represents 2% of global production [2]
Latin America only represents 2% of global production [2]. This low representation in rubber production is mainly limited by the principal phytosanitary problem of this crop, a foliar disease known as South American leaf blight, which is caused by the fungus Pseudocercospora ulei [4] that affects the physiology of the plant [5,6] and gradually reduces latex production [7]
Our results showed a desirable ability in the tuned models to classify the South American Leaf Blight (SALB) levels independently of phenology or genotype, similar to that reported by Sterling and Melgarejo [20], who used multiple regression techniques on spectral vegetation indices and found no influence of phenology when discriminating SALB symptoms

Summary

Introduction

Latin America only represents 2% of global production [2] This low representation in rubber production is mainly limited by the principal phytosanitary problem of this crop, a foliar disease known as South American leaf blight, which is caused by the fungus Pseudocercospora ulei [4] that affects the physiology of the plant [5,6] and gradually reduces latex production (reduction from 20 to 75%) [7]. This disease is managed mainly by genetic control, and monitoring is carried out using classic measurement methods [8]

Objectives

Methods

Results

Discussion

Conclusion