Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves

Abstract

Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (>95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R²cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161mg/cm². Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R²CV=0.70 and RMSECV=0.0015mg/cm²). The spectral domain 400–900nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring.

Plant phenotyping relevance

凍害状態のハイパースペクトル検出と葉の水分・色素含量推定手法を開発・評価しており、植物表現型の取得・抽出が研究の中心である。

Code and data availability

The supplied blocks describe laboratory hyperspectral measurements of freezing-treated oilseed rape leaves and PCR/PLSR/SVM analysis, but contain no data availability statement, no public phenotype/spectral dataset, and no authors' code repository. The only URLs present (LIBSVM, CiteSeer) are generic third-party tool/c

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