Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (13): 2220-2229.doi: 10.3864/j.issn.0578-1752.2019.13.003

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Cotton Nitrogen Nutrition Diagnosis Based on Spectrum and Texture Feature of Images from Low Altitude Unmanned Aerial Vehicle

CHEN PengFei1,2,LIANG Fei3   

  1. 1 Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences/State Key Laboratory of Resources and Environmental Information System, Beijing 100101
    2 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023
    3 Institute of Farmland Water Conservancy and Soil Fertilizer, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, Xinjiang
  • Received:2019-02-25 Accepted:2019-03-14 Online:2019-07-01 Published:2019-07-11

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Abstract:

【Objective】 Based on the high spatial resolution images of unmanned aerial vehicle (UAV), the effects of removing soil background information and increasing image texture information on the inversion of cotton plant nitrogen concentration were investigated, in order to provide new technology for accurate estimation of cotton nitrogen nutrition status. 【Method】 Cotton water and nitrogen coupling experiment was conducted, and UAV images and plant nitrogen concentration data were measured during different cotton growth stages. Based on the above data, the effect of soil background on cotton canopy spectrum was firstly investigated. Secondly, the correlations between image texture parameters and plant nitrogen concentration were analyzed. Finally, the obtained data was divided into calibration dataset and validation dataset. Different scenarios, including before and after removing the soil background, and adding texture features, were set. The inversion models of plant nitrogen concentration under various scenarios were designed by using the coupled method of spectral indexes and principal component regression, and the performances of the models were compared. 【Result】 The soil background had an effect on the cotton canopy spectrum, and the trends were not the same at different growth stages. There existed significant correlations between image texture parameters and plant nitrogen concentration. For the scenarios before removal soil background, the plant nitrogen concentration prediction model had determination coefficient (R 2) value of 0.33 and root mean square error (RMSE) value of 0.21% during model calibration, and R 2 value of 0.19 and RMSE value of 0.23% during validation. For the scenarios after removing soil background, the plant nitrogen concentration prediction model had R 2 value of 0.38 and RMSE value of 0.20% during model calibration, and R 2 value of 0.30 and RMSE value of 0.21% during validation. For the scenarios adding image texture information, the plant nitrogen concentration prediction model had R 2 value of 0.57 and RMSE value of 0.17% during model calibration, and R 2 value of 0.42 and RMSE value of 0.19% during validation. 【Conclusion】 Based on high spatial resolution images of low-altitude UAVs, both removing soil background and adding image texture information could improve the inversion accuracy of cotton plant nitrogen concentration. Image texture could be considered as important information to support prediction of crop nitrogen nutrition status using UAV images.

Key words: unmanned aerial vehicle (UAV), multi-spectra, image texture feature, nitrogen nutrition diagnosis, cotton

Table 1

Irrigation time of integrated irrigation of water and fertilizer and the proportion of each component applied to the total amount at each irrigation time"

项目
Item		 施肥日期（月-日） Fertilization date (M-D)
6-23 7-04 7-12 7-22 8-02 8-11 8-27 9-05
氮肥施用比例 Nitrogen fertilizer ratio 10% 15% 20% 20% 15% 10% 10% 0
磷、钾肥施用比例 Phosphorus and potassium fertilizer ratio 5% 10% 10% 15% 20% 20% 15% 5%
灌水比例 Water ratio 10% 15% 15% 15% 15% 15% 10% 5%

Fig. 1

Used UAV in this study and one captured image"

Table 2

Used spectral indices in this study"

光谱指数 Spectral index 公式 Formula 发明者 Developed by
以归一化植被指数为构型的各光谱指数
Normalized Difference Vegetation Index Like Indices,NDVIs		 (Ri-Rj)/(Ri+Rj)
 ROUSE等[19]
比值植被指数 Ratio Vegetation Index,RVI Rnir /Rred PEARSON等[20]
增强植被指数 Enhanced Vegetation Index,EVI 2.5×(Rnir-Rred)/(Rnir+6×Rred-7.5×Rblue+1) HUETE等[21]
三角植被指数 Triangular Vegetation Index,TVI 0.5×(120×(Rnir-Rgreen) - 200×(Rred-Rgreen)) BROGE等[22]
改进土壤调整植被指数
Modified Soil-Adjusted Vegetation Index,MSAVI		 (2×Rnir+1-sqrt((2×Rnir+1)2-8×(Rnir-Rred)))/2 QI等[23]
土壤调整植被指数
Optimization of Soil-Adjusted Vegetation Index,OSAVI		 1.16×(Rnir -Rred)/(Rnir+Rred+0.16) RONDEAUX等[24]
修改三角植被指数
Modified Triangular Vegetation Index 2,MTVI2		 1.5×(1.2×(Rnir-Rgreen)-2.5×(Rred - Rgeen))/sqrt((2×Rnir+1)2 - (6×Rnir-5×sqrt(Rred)) - 0.5) HABOUDANCE等[25]
红边模型 Red Model,R-M Rnir/Rred-edge-1 GITELSON等[26]
绿波段比值植被指数 Green Ratio Vegetation Index,RVIgreen Rnir /Rgreen XUE等[27]

Fig. 2

True color synthetic images of cotton during different growth stages and changes in reflectance before and after soil background removal in corresponding periods A, B: Bud; C, D: Early bloom; E, F: Peak bloom"

Fig. 3

Absolute value of correlation coefficient between different texture features and plant nitrogen concentration in different bands"

Fig. 4

Prediction results of cotton plant nitrogen concentration under different scenarios A: Calibration results before removing soil background; B: Validation results before removing soil background; C: Calibration results after removing soil background; D: Validation results after removing soil background; E: Calibration results after removing soil background and adding texture information; F: Validation results after removing soil background and adding texture information"

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