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| Prediction of Total Nitrogen Content of Lettuce Based on UAV
Multi-Spectral Vegetation Index |
| LIAN Bing-rui1, 2, LI Ya-hao1, 3, ZHANG Jing2, LI Chang-qing4, YANG Xiao-dong5*, WANG Ji-qing2, ZOU Guo-yuan1, Thompson Rodney6, YANG Jun-gang1* |
1. Institute of Plant Nutrition and Resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2. College of Agriculture and Forestry, Hebei North University, Zhangjiakou 075000, China
3. Beijing Cuihu Agricultural Technology Company, Beijing 100089, China
4. College of Resources and Environmental Sciences, Hebei Agricultural University, Baoding 071000, China
5. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
6. University of Almeria, Spain 04120
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Abstract China has a huge and intensive open-field vegetable production system. However, serious issues such as low water and nitrogen use efficiencies, as well as excessive fertilizer application, limit the sustainability of the system. To improve the efficiency of production and enhance accurate fertilization in large-scale vegetable cultivation systems, this study was conducted with open-field lettuce. Three treatments of no nitrogen (N0), low nitrogen (N1) and high nitrogen (N2) were established. An unmanned aerial vehicle (UAV) equipped with a multi-spectral camera was used to establish correlations between three multi-spectral vegetation indices (NDVI, RVI, and SAVI) and lettuce chlorophyll content, biomass, crop nitrogen uptake, and total nitrogen content. Models to predict total nitrogen content for single growth stage and multiple growth stages were developed. The results showed that: (1) during rosette and heading stages, NDVI, RVI and SAVI values increased with the amount of applied nitrogen, but that during harvest stage, maximum values occurred with the N1 treatment; (2) NDVI showed a significant correlation with lettuce yield, nitrogen uptake and chlorophyll content during heading stage; and the total nitrogen content of lettuce was significantly correlated with chlorophyll content at p<0.01 level, with a correlation coefficient (R) of 0.51. When considering multiple growth stages together, NDVI values showed a significant correlation with lettuce yield, chlorophyll content, nitrogen uptake, and total nitrogen content at p<0.001 level, with correlation coefficients of 0.85, 0.82, 0.81, and 0.71, respectively. (3) Relationships of exponential, linear, logarithmic and power functions were fitted to the corresponding datasets, and the best prediction model of total nitrogen content for lettuce (N%=16.52ln(NDVI)+73.514) was established in the multiple growth stages. Using the lettuce total nitrogen content prediction model to obtain modeled values of total nitrogen content for the area of a commercial production field on the same farm, the average relative error was 3.2%, RMSE=0.556 6, NRMSE=0.010 8, showing accurate estimation of total nitrogen content. The results show that the model had good accuracy and that it is feasible to diagnose vegetable nitrogen content using unmanned aerial vehicle multi-spectral remote sensing.
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Received: 2023-01-31
Accepted: 2023-09-27
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Corresponding Authors:
YANG Xiao-dong, YANG Jun-gang
E-mail: yangxd@nercita.org.cn; jungangyang@163.com
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