Estimation of cotton SPAD value and leaf water content based on UAV multispectral images

被引：0

作者：

Yan C. ^{[1
]}

Qu Y. ^{[1
]}

Chen Q. ^{[1
]}

Wu H. ^{[2
]}

Zhang B. ^{[2
]}

Peng H. ^{[1
]}

Chen Q. ^{[1
]}

机构：

[1] College of Agronomy, Xinjiang Agricultural University, Engineering Research Center of Cotton, Ministry of Education, Urumqi

[2] College of Pratacultural and Environmental Sciences, Xinjiang Agricultural University, Urumqi

来源：

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2023年 / 39卷 / 02期

关键词：

agricultural monitoring; cotton; drought stress; leaf water content; multispectral; SPAD value; UAV;

D O I：

10.11975/j.issn.1002-6819.202208002

中图分类号：

学科分类号：

摘要：

An accurate and rapid detection has been one of the most important steps to monitor the drought stress status of cotton at flowering and boll-forming stage in the cotton yield and quality. This study aims to realize the rapid detection of key drought resistance indexes of cotton. 253 cotton resources were taken as the raw materials in this experiment. Two treatments were set for the drought stress and normal irrigation. The images were also obtained by the DJI Jingling 4 multispectral UAV at the flowering and boll-forming stage. A systematic analysis was made to determine the spectral reflectance of drought stress and normal treatment cotton at the flowering and boll-forming stage. The predicted model was established to combined with the relative value of chlorophyll (SPAD) and leaf water content (LWC) data of ground survey using the radial basis function in the neural network algorithm. The results showed that the red-light reflection value of cotton increased slightly before and after drought stress, whereas, the other four spectra were less than normal treatment. The best estimation models were cubic function to predict the SPAD by radial basis function. The best estimation models of LWC were quadratic function. The ideal model of quadratic function SPAD was y=0.010 6x2-0.673x+61.045, where determination coefficient R2 was 0.8488, root mean square error RMSE was 2.005 and relative error RE was 0.004. The ideal model of LWC was y=-0.017 6x2+0.709 7x+3.249 3, where R2 was 0.9366, RMSE was 0.930, and RE was 0.011. The improved model was also applied to discriminate the drought image in the experimental area. The SPAD and LWC prediction values were clustered and analyzed during this time. The drought classification based on SPAD clustering results was that extreme drought, severe drought, moderate drought, light drought and no drought, and the drought classification based on LWC clustering results was that extreme drought, severe drought, moderate drought, drought, light drought and no drought. The obtained models and classification effects were better conform to the characteristics of cotton drought stress. The finding can provide a strong reference for the rapid and accurate acquisition during cotton drought monitoring. © 2023 Chinese Society of Agricultural Engineering. All rights reserved.

引用

页码：61 / 67

页数：6

共 31 条

[1] SONG Junqiao, SUN Peijun, ZHANG Xia, Et al., Screening of high oil materials of cotton kernels and their fatty acid development analysis, Cotton Science, 22, 4, pp. 291-296, (2010)
[2] LIANG Yajun, LI Yuhua, WANG Junduo, Et al., Xinjiang 2020 cotton production situation and existing problems and strategies, Cotton Science, 43, 5, pp. 16-23, (2021)
[3] ZHANG Fang, YAN An, RE Yi, Et al., Comprehensive evaluation of drought resistance of winter wheat during germination in Xinjiang, Journal of Plant Genetic Resources, 20, 1, pp. 100-112, (2019)
[4] SANG Xiaohui, Comprehensive Evaluation of Drought Resistance and Exploration of Molecular Markers Related to Drought Resistance in Upland Cotton, (2017)
[5] HAN Huiling, KANG Fengjun, Effects of water stress on cotton production, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 17, 3, pp. 37-40, (2001)
[6] WEN Ming, LI Minghua, JIANG Jiale, Et al., Effects of nitrogen, phosphorus and potassium application modes on the growth and yield of drip-irrigated cotton in northern Xinjiang, Chinese Agricultural Sciences, 54, 16, pp. 3473-3487, (2021)
[7] LIU Zhaowei, ZHANG Pan, WANG Rui, Et al., Effects of continuous soil drought on gas exchange parameters and chlorophyll fluorescence characteristics of cotton boll subtending leaves, Applied Ecology, 25, 12, pp. 3533-3539, (2014)
[8] GUAN Lijun, SUN Jianting, WANG Xiaowei, Et al., The effects of different topping agents on the growth and development of cotton in Xinjiang, Anhui Agricultural Sciences, 42, 6, pp. 1655-1656, (2014)
[9] TIAN Yousheng, FAN Shuli, PANG Chaoyou, Et al., Effects of drought stress during the whole growth period on cotton morphology, physiology, photosynthesis and yield, North China Agricultural Journal, 32, 5, pp. 224-231, (2017)
[10] ZHAO Yandong, GAO Chao, ZHANG Xin, Et al., Advances in real-time online detection methods of plant water stress, Transactions of the Chinese Society for Agricultural Machinery, 47, 7, pp. 290-300, (2016)

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