Quantitative Monitoring Models of Potato Leaf Water Content in Tuber Formation Period Based on Hyperspectral Characteristic Parameters
Suyala Qiqige1*, ZHANG Zhen-xin2, LI Zhuo-ling1, FAN Ming-shou2, JIA Li-guo2, ZHAO Jin-hua1
1. Department of Botany, College of Grassland and Resource Environment, Inner Mongolia Agricultural University, Huhhot 010019, China
2. Department of Plant Physiology, College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China
Abstract：A reasonable water supply is a prerequisite for potatoes to achieve high yield and high-quality tubers. To realize the rapid water diagnosis during the critical period of potato water demand, we used hyperspectral remote sensing and machine learning to study the real-time monitoring of plant water status during the potato tuber formation period to lay the foundation for efficient water management of potatoes in arid areas. This article aims to construct a quantitative estimation model for leaf water content during potato tuber formation with high monitoring accuracy and greater universality. The data of canopy hyperspectral reflectance and leaf water content were measured, and the characteristic spectral parameters that respond to the moisture content of potato leaf finally constructed the Partial least squares regression, Support vector machine, and BP neural network models of leaf water content based on the hyperspectral characteristic parameters. The results showed that: For the monitoring of potato leaf water content, screened the 13 sensitive bands such as 725, 856, 1 000 nm, etc; 11 characteristic spectral first-order derivatives such as 521, 555, 570 nm, etc.; and 7 characteristic spectral indices such as MSI, NDII, PSRI, etc. The three established models that are based on the above characteristic spectral parameters can accurately quantify the leaf water content of potatoes in the tuber formation stage, which means these combined spectral characteristic parameters have strong practicality; Moreover, the use of characteristic spectral parameters screened from full growth stage leaf moisture content and hyperspectral data had higher universality in quantitative monitoring of leaf water content in potato during the critical growth stages. The BP neural network model had the highest prediction accuracy in monitoring leaf moisture content during the tuber formation period. Therefore, this study's results can monitor potato leaves' water content in real-time and accurately, which was of great value for evaluating the water status of potato plants and providing technical support for rapid water diagnosis and water-saving irrigation recommendations for potatoes.
Key words：Hyperspectral; Characteristic parameters; Potato; Leaf water content; Quantitative detection
苏亚拉其其格，张振鑫，李卓凌，樊明寿，贾立国，赵金花. 基于高光谱特征参数的马铃薯块茎形成期叶片含水量定量监测模型[J]. 光谱学与光谱分析, 2025, 45(03): 774-783.
Suyala Qiqige, ZHANG Zhen-xin, LI Zhuo-ling, FAN Ming-shou, JIA Li-guo, ZHAO Jin-hua. Quantitative Monitoring Models of Potato Leaf Water Content in Tuber Formation Period Based on Hyperspectral Characteristic Parameters. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 774-783.
[1] Hardigan M A, Laimbeer F P E, Newton L, et al. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(46): E9999.
[2] YU Bin, YANG Hong-yu, WANG Li, et al(余 斌, 杨宏羽, 王 丽, 等). Acta Agronomica Sinica(作物学报), 2018, 44(1): 63.
[3] Wang Y, Liu X, Ren G, et al. Agricultural and Forest Meteorology, 2019, 264: 334.
[4] LIU Rui-fang, YANG Jian, WU Ri-na, et al(刘瑞芳, 杨 健, 乌日娜, 等). Journal of Northern Agriculture(北方农业学报), 2016, 44(2): 5.
[5] XIAO Hou-jun, SUN Rui-feng, HE Jia-fang, et al(肖厚军, 孙锐锋, 何佳芳, 等). Guizhou Agricultural Sciences(贵州农业科学), 2011, 39(1): 73.
[6] Deng R R, He Y Q, Qin Y, et al. Journal of Remote Sensing, 2012, 16(1): 192.
[7] LIANG Liang, ZHANG Lian-peng, LIN Hui, et al(梁 亮, 张连蓬, 林 卉, 等). Scientia Agricultura Sinica(中国农业科学), 2013, 46(1)：18.
[8] Peñuelas J, Filella I, Biel C, et al. International Journal of Remote Sensing, 1993, 14(10): 1887.
[9] Gao B C. Remote Sensing of Environment, 1996, 58(3)：257.
[10] Zhao S, Wang Q, Yao Y, et al. Journal of Agricultural Science and Technology, 2016, 18: 387.
[11] Wang J, Xu R, Yang S. Environmental Monitoring & Assessment, 2009, 157(1-4): 459.
[12] Baghzouz M, Devitt D A, Morris R L. Applied Engineering in Agriculture, 2007, 23(6): 763.
[13] Gopal K, Rabi N S, Prafull S, et al. Agricultural Water Management, 2019, 213: 231.
[14] LIU Ming-bo, TANG Yan-lin, LI Xiao-li, et al(刘明博, 唐延林, 李晓利, 等). Infrared and Laser Engineering(红外与激光工程), 2014, 43(4): 1265.
[15] Araújo M C U, Saldanha T C B, Galvao R K H, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65.
[16] WANG Gui-zeng, YE Hao(王桂增, 叶 昊). Principal Component Analysis and Partcal Least Squares Method(主元分析与偏最小二乘法). Beijing：Tsinghua University Press(北京：清华大学出版社), 2012.
[17] QIAN Fei, JIANG Wei-zhong, CHE Zhao-jian, et al(钱 飞, 蒋卫中, 车兆建, 等). Urban Rail Transit Research(城市轨道交通研究), 2014, 17(12): 86.
[18] CHENG Jia-wei, XU Jia, WANG Yi-ling, et al(程嘉蔚, 徐 佳, 王艺玲, 等). Modern Electronic Technology(现代电子技术), 2021，44(19): 178.
[19] WANG Jin, LI Xin-jian, BAI Li, et al(王 进, 李新建, 白 丽, 等). Chinese Journal of Agrometeorology(中国农业气象), 2012, 33(1): 114.
[20] YI Xiang, ZHANG Li-fu, LÜ Xin, et al(易 翔, 张立福, 吕 新, 等). Cotton Science(中国棉花), 2021, 33(3): 224.
[21] Li Y, Demetriades-Shah T H, Kanemasu E T, et al. Remote Sensing of Environment, 1993, 44(1): 81.
[22] LI Yuan-yuan, CHANG Qing-rui, LIU Xiu-ying, et al(李媛媛, 常庆瑞, 刘秀英, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(16): 135.
[23] CHE Yong-fei, ZHAO Ying-jun, YI Pei-yuan, et al(车永飞, 赵英俊, 伊丕源, 等). Science Technology and Engineering(科学技术与工程), 2014, (34): 5.
[24] LIU Xiu-ying, YU Jun-ru, WANG Shi-hua(刘秀英, 余俊茹, 王世华). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(22): 308.
[25] Shao X G, Bian X H, Liu J J, et al. Analytical Methods, 2010, 2(2): 1662.
