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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.
Fu P, Meacham-Hensold K, Guan K, Bernacchi CJ. Hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Front Plant Sci. 2019;10:730.
Ge Y, Atefi A, Zhang H, Miao C, Ramamurthy RK, Sigmon B, Yang J, Schnable JC. High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: A case study with a maize diversity panel. Plant Methods. 2019;15:66–66.
Alazem M, Lin N-S. Antiviral roles of abscisic acid in plants. Front Plant Sci. 2017;8.
Chen K, Li GJ, Bressan RA, Song CP, Zhu JK, Zhao Y. Abscisic acid dynamics, signaling, and functions in plants. J Integr Plant Biol. 2020;62:25–54.
Bharath P, Gahir S, Raghavendra AS. Abscisic acid-induced stomatal closure: An important component of plant defense against abiotic and biotic stress. Front Plant Sci. 2021;12.
Cheng W-H, Endo A, Zhou L, Penney J, Chen HC, Arroyo A, Leon P, Nambara E, Asami T, Seo M, et al. A unique short-chain dehydrogenase/reductase in Arabidopsis glucose signaling and abscisic acid biosynthesis and functions. Plant Cell. 2002;14:2723–2743.
Lin PC, Hwang SG, Endo A, Okamoto M, Koshiba T, Cheng WH. Ectopic expression of ABSCISIC ACID 2/GLUCOSE INSENSITIVE 1 in Arabidopsis promotes seed dormancy and stress tolerance. Plant Physiol. 2007;143:745–758.
Song Y, Xiang F, Zhang G, Miao Y, Miao C, Song CP. Abscisic acid as an internal integrator of multiple physiological processes modulates leaf senescence onset in Arabidopsis thaliana. Front Plant Sci. 2016;7:181.
Verslues PE, Bray EA. Role of abscisic acid (ABA) and Arabidopsis thaliana ABA-insensitive loci in low water potential-induced ABA and proline accumulation. J Exp Bot. 2006;57:201–212.
Gibson SI. Sugar and phytohormone response pathways: Navigating a signalling network. J Exp Bot. 2004;55:253–264.
Fàbregas N, Fernie AR. The metabolic response to drought. J Exp Bot. 2019;70:1077–1085.
Guo R, Shi LX, Jiao Y, Li MX, Zhong XL, Gu FX, Liu Q, Xia X, Li HR. Metabolic responses to drought stress in the tissues of drought-tolerant and drought-sensitive wheat genotype seedlings. AoB Plants. 2018;10(2):Article ply016.
Mundim FM, Pringle EG. Whole-plant metabolic allocation under water stress. Front Plant Sci. 2018;9:852.
Wang M, Lee J, Choi B, Park Y, Sim HJ, Kim H, Hwang I. Physiological and molecular processes associated with long duration of ABA treatment. Front Plant Sci. 2018;9:176.
Roper JM, Garcia JF, Tsutsui H. Emerging technologies for monitoring plant health in vivo. ACS Omega. 2021;6:5101–5107.
Gutiérrez Rodríguez BJ, Argüello Tovar JO, García Navarrete ÓL. Use of VIS-NIR-SWIR spectroscopy for the prediction of water status in soybean plants in the Colombian Piedmont Plains. Dyna. 2019;86(210):125–130.
Woolley JT. Reflectance and transmittance of light by leaves. Plant Physiol. 1971;47(5):656–662.
Carter GA. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int J Remote Sens. 1994;15(3):697–703.
Maccioni A, Agati G, Mazzinghi P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol B Biol. 2001;61(1–2):52–61.
Main R, Cho MA, Mathieu R, O’Kennedy MM, Ramoelo A, Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogramm Remote Sens. 2011;66(6):751–761.
Peñuelas J, Filella I, Biel C, Serrano L, Savé R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens. 1993;14(10):1887–1905.
Peñuelas J, Gamon JA, Griffin KL, Field CB. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sens Environ. 1993;46(2):110–118.
Dash J, Curran PJ. The MERIS terrestrial chlorophyll index. Int J Remote Sens. 2004;25:5403–5413.
Gitelson AA, Keydan GP, Merzlyak MN. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett. 2006;33(11):Article L11402.
Gitelson AA, Merzlyak MN, Chivkunova OB. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol. 2001;74(1):38–45.
Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sens Environ. 2011;115(2):281–297.
le Maire G François C, Soudani K, Berveiller D, Pontailler J, Bréda N, Genet H, Davi H, Dufrêne E. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sens Environ. 2008;112(10):3846–3864.
Blackburn GA. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. Int J Remote Sens. 1998;19(4):657–675.
Chen P, Haboudane D, Tremblay N, Wang J, Vigneault P, Li B. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens Environ. 2010;114(9):1987–1997.
Daughtry C, Walthall CL, Kim MS, Brown de Colstoun E, McMurtrey JE Ⅲ. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ. 2000;74(2):229–239.
Neff MM, Chory J. Genetic interactions between phytochrome A, phytochrome B, and cryptochrome 1 during Arabidopsis development. Plant Physiol. 1998;118(1):27–35.
Laby RJ, Kincaid MS, Kim D, Gibson SI. The Arabidopsis sugar-insensitive mutants sis4 and sis5 are defective in abscisic acid synthesis and response. Plant J. 2000;23(5):587–596.
Giordano M, el-Nakhel C, Pannico A, Kyriacou MC,Stazi SR, de Pascale S, Rouphael Y. Iron biofortification of red and green pigmented lettuce in closed soilless cultivation impactscrop performance and modulates mineral and bioactive composition. Agronomy. 2019;9(6):290.
Lichtenthaler HK, Wellburn AR. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Biochem Soc Trans. 1983;11(5):591–592.
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