Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.

Abstract

Chinese fir is the most important native softwood tree in China and has significant economic and ecological value. Accurate assessment of the growth status is critical for both seedling cultivation and germplasm evaluation of this commercially significant tree. Needle leaf chlorophyll content (LCC) and needle leaf water content (LWC), which are determinants of plant health and photosynthetic efficiency, are important indicators of the growth status in plants. In this study, for the first time, the LCC and LWC of Chinese fir seedlings were estimated based on hyperspectral reflectance spectra and machine learning algorithms. A line-scan hyperspectral imaging system with a spectral range of 870 to 1,720 nm was used to capture hyperspectral images of seedlings with varying LCC and LWC. The spectral data of the canopy area of the seedlings were extracted and preprocessed using the Savitzky-Golay smoothing (SG) algorithm. Subsequently, the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) methods were employed to extract the most informative wavelengths. Moreover, SVM, PLSR and ANNs were utilized to construct models that predict LCC and LWC based on effective wavelengths. The results indicated that the CARS-ANNs were the best for predicting LCC, with R² C = 0.932, RSME C = 0.224, and R² P = 0.969, RSME P = 0.157. Similarly, the SPA-ANNs model exhibited the best prediction performance for LWC, with R² C = 0.952, RSME C = 0.049, and R² P = 0.948, RSME P = 0.051. In conclusion, the present study highlights the significant potential of combining hyperspectral imaging (HSI) with machine learning algorithms as a rapid, non-destructive, and highly accurate method for estimating LCC and LWC in Chinese fir.

Plant phenotyping relevance

ハイパースペクトル画像と機械学習により、苗木の葉緑素量・含水量という植物形質を非破壊推定する手法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The supplied article blocks describe hyperspectral imaging of Chinese fir seedlings and machine-learning models for LCC/LWC prediction, but contain no data availability statement, no public dataset or image deposit, and no code availability or repository URL. The only URL present is the CC BY 4.0 license link, which is

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