Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles.

ABSTRACT: Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares d

SUBMITTER: Conrad AO 

PROVIDER: S-EPMC7706329 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature