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| Hyperspectral Detection and Visualization of Pigment Content in
Different Positions of Tomato Leaf at Seadling Stage |
| ZHAO Jian-gui1, WANG Guo-liang1, 2, ZHANG Yu1, ZHAO Li-jie3, CHEN Ning1, WANG Wen-jun1, DU Hui-ling3, LI Zhi-wei1* |
1. College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China
2. Millet Research Institute, Shanxi Agricultural University, Changzhi 046000, China
3. Basic Department, Shanxi Agricultural University, Taigu 030801, China
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Abstract The leaf pigment content is an important indicator to characterize crop cultivation substrate's nutrient elements and physiological state. Rapid and accurate acquisition of pigment content and leaf position distribution is the basis for precise water and fertilizer management in facility agriculture. Chlorophyll a (Chla), Chlorophyll b (Chlb), Chlorophyll (Chll) and Carotenoid (Caro) at different leaf positions of the tomato seedling stage were used as research indicators. Ten nitrogen concentrations were prepared for the nutrient solution. We picked 1710 slices (285 samples) for VIS-NIR hyperspectral acquisition according to the leaf position. The data were preprocessed by Savitzky-Golay (S-G), standard normal variate (SNV), and multiple scattering correction (MSC). Firstly, the key bands were “roughly” extracted with the competitive adaptive reweighted sampling (CARS) algorithm. Then, the iteratively retains informative variables (IRIV) algorithm to judge the importance of key bands, and “accurately” extract the optimal set of bands by reverse eliminating strong and weak bands. The partial least squares regression (PLSR) models were established. The results showed that: (1) When the nutrient liquid nitrogen concentration was 302.84 mg·L-1, the leaf pigment content was the largest. Moreover, the inhibitory effect of high concentration is higher than that of low concentration. The pigment content in the leaf position showed the distribution law of upper>middle>low. (2) The CARS-IRIV-PLSR algorithm, “roughly-accurately” key band screening strategy was used to extract 4, 4, 10 and 11 key bands for Chla, Chlb, Chll and Caro, and the Rp was 0.772 2, 0.732 1, 0.847 1 and 0.858 7, respectively. (3) Combined with the optimal model pigment quantitative inversion image visual expression, the distribution rules of Chla, Chlb and Chll are consistent, while the distribution rules of Caro and Chll are opposite. This conclusion is consistent with the plant's physiological characteristics and measurement results. The hyperspectral imaging technology can realize the nondestructive detection and visual expression of leaf pigment content. It provides data support and a theoretical basis for plant leaf pigment distribution, nutrient deficiency and fertilization decision-making in facility agriculture.
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Received: 2022-07-05
Accepted: 2022-10-22
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Corresponding Authors:
LI Zhi-wei
E-mail: lizhiweitong@163.com
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[1] Wang N, Fu F, Wang H, et al. Scientific Reports, 2021, 11(1): 16651.
[2] Pérez-Patricio M, Camas-Anzueto J L, Sanchez-Alegría A, et al. Sensors, 2018, 18(2): 650.
[3] ZHAO Kun, WANG Jun-ke, WANG Chu-feng, et al(赵 琨, 王珺珂, 王楚锋, 等). Journal of Huazhong Agricultural University(华中农业大学学报), 2018, 37(4): 78.
[4] SUN Hong, ZHENG Tao, LIU Ning, et al(孙 红, 郑 涛, 刘 宁, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(1): 149.
[5] YU Ke-qiang, ZHAO Yan-ru, LI Xiao-li, et al(余克强, 赵艳茹, 李晓丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(3): 746.
[6] Sari B G, Lúcio A D C, Santana C S, et al. Scientia Horticulturae, 2019, 246: 146.
[7] General Adminstration of Quality Supervision, Inspection and Quarantine of the People's Repbulic of China/Standardization Administration of the People's Repbulic of China(中华人民共和国国家质量监督检验检疫总局/中国国家标准化管理委员会). National Standard of the People's Repbulic of China GB/T 22182—2008/ISO 10519: 1997 Rapeseed-Determination of Chlorophyll Content-Spectrometric Method(中华人民共和国国家标准: GB/T 22182—2008/ISO 10519: 1997油菜籽叶绿素含量测定分光光度计法)，2008.
[8] Rinawati M, Sari L A, Pursetyo K T. IOP Conference Series: Earth and Environmental Science, 2020, 441(1): 012056.
[9] TIAN Ping, LI Jian-she, GAO Yan-ming(田 萍，李建设，高艳明). Journal of Zhejiang University (Agriculture and Life Sciences)[浙江大学学报(农业与生命科学版)], 2018, 44(6): 667.
[10] Saad A G, Azam M M, Amer B M A. Food Analytical Methods, 2022, 15(3): 689.
[11] Zhu W, Sun Z, Yang T, et al. Computers and Electronics in Agriculture, 2020, 178: 105786.
[12] Sonobe R, Yamashita H, Mihara H, et al. International Journal of Remote Sensing, 2021, 42(4): 1311.
[13] Yun Y H, Bin J, Liu D L, et al. Analytica Chimica Acta, 2019, 1058: 58.
[14] Wang Y, Hu X, Jin G, et al. Journal of the Science of Food and Agriculture, 2019, 99(4): 1997.
[15] Hashimoto Y, Suzuki H, Kondo T, et al. Journal of Photochemistry and Photobiology A: Chemistry, 2022, 426: 113750.
[16] Torres-Montilla S, Rodriguez-Concepcion M. Progress in Lipid Research, 2021, 84: 101128.
[17] Lin Y P, Charng Y. Plant Science, 2021, 302: 110682.
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