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

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 CO 2 assimilation rate ( A ) and extrinsic water use efficiency ( WUEe ) in the training (R 2 ranged from 0.97 to 0.99) and testing (R 2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R 2 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.

Plant phenotyping relevance

葉のハイパースペクトル反射と機械学習を用いて、ゴム樹の病害レベルおよび病害による光合成形質を推定・分類する手法が研究の中心である。

Code and data availability

The paper's hyperspectral reflectance and photosynthetic trait measurements (120 spectral samples, nine traits) are not publicly deposited; the Data Availability Statement says they are available only upon request. The MDPI supplementary material contains only model tuning tables and result plots, not the underlying or

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