Hyperspectral assessment of plant responses to multi‐stress environments: Prospects for managing protected agrosystems

Abstract

Societal Impact Statement Advancements in our ability to rapidly detect plant responses to stress are necessary to improve crop management practices and meet the global challenge of food security. Using optical approaches to detect plant stress before symptoms become apparent has great potential, but these approaches lack testing in multiple‐stress environments and fail to fully exploit the data collected. Using hyperspectral data from lettuce, we show that optical measurements can provide growers with important stress‐related information to inform crop management practices. We suggest that integrating this technology into protected agrosystems, such as greenhouses, could greatly improve crop quality and yield. Summary Tools to detect and predict stress pre‐visually are essential to optimally manage agrosystems. Here, we investigated the capability of reflectance spectroscopy to characterize responses of asymptomatic crop leaves under multi‐stress conditions. Full range (350–2,500 nm) reflectance measurements and traditional plant stress responses were collected on lettuce leaves under the combination of different supplemental light types and intensities, fertilization and salinity. Partial least‐squares discriminate analysis and regression modeling and spectral indices were employed to characterize plant responses to multiple stress conditions, both alone and in combination. Spectral profiles (400–800 nm + 1,900–2,200 nm) of individuals grown under variable environments were statistically different ( p R 2 : 0.70–0.84). Higher lettuce yield and quality was found under sodium light at high intensity (850 µmol m −2 s −1 photosynthetic active radiation), with high fertilization (150 ppm N) and no salinity. Our findings highlight the utility and limitations of vegetation spectroscopy in a protected agrosystem. We suggest that integration of vegetation spectroscopy into intelligent and automated greenhouses and other protected systems could enhance management efficiency, as well as crop quality and yield.

Plant phenotyping relevance

レタス葉のストレス応答を反射分光で測定し、スペクトル解析・回帰モデルで植物状態を推定する方法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

Code and data availability

The supplied blocks describe hyperspectral measurements and PLSR/PLS-DA analyses of lettuce, but contain no data availability statement, deposited dataset, author code repository, or trained model release. Mentions of R packages ('pls', 'caret', 'vegan') are generic libraries, not paper-specific assets, and no public,

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