Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
作者机构：Temasek Life Sciences LaboratoryNational University of SingaporeSingaporeSingapore Department of Biological SciencesNational University of SingaporeSingaporeSingapore Translational Biophotonics LaboratoryInstitute of Bioengineering and BioimagingAgency for ScienceTechnology and Research(A*STAR)SingaporeSingapore Institute of Materials Research and Engineering(IMRE)Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2023年第5卷第3期
页 面：474-483页
核心收录：
学科分类：08[工学] 081104[工学-模式识别与智能系统] 0903[农学-农业资源与环境] 0706[理学-大气科学] 0901[农学-作物学] 0811[工学-控制科学与工程]
基 金：supported by the Agency for Science,Technology and Research(A*STAR)Singapore under the industry alignment fund prepositioning program High Performance Precision Agriculture system(A19E4a0101).
主 题：drought reflectance indices
摘 要：Leaf color patterns vary depending on leaf age,pathogen infection,and environmental and nutritional stresses;thus,they are widely used to diagnose plant health statuses in agricultural fields.The visible-near infrared-shortwave infrared(VIS-NIR-SWIR)sensor measures the leaf color pattern from a wide spectral range with high spectral resolution.However,spectral information has only been employed to understand general plant health statuses(e.g.,vegetation index)or phytopigment contents,rather than pinpointing defects of specific metabolic or signaling pathways in plants.Here,we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics,pinpointing physiological alterations associated with the stress hormone,abscisic acid(ABA).Leaf reflectance spectra of wild-type,ABA2-overexpression,and deficient plants were collected under watered and drought conditions.Drought-and ABA-associated normalized reflectance indices(NRIs)were screened from all possible pairs of wavelength bands.Drought associated NRIs showed only a partial overlap with those related to ABA deficiency,but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range.Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices.Major selected NRIs were independent from leaf water content and chlorophyll content,2 well-characterized physiological changes under drought.The screening of NRIs,streamlined with the development of simple classifiers,serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.
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