• 专刊：无人机在生态学和地理学中的应用 •

### 利用无人机的多光谱参数预测荔枝叶片养分质量分数

1. （1. 昆明理工大学农业与食品学院，昆明 650000；2. 广州地理研究所//广东省地理空间信息技术与应用公共实验室// 广东省遥感与地理信息系统应用重点实验室，广州 510070）
• 出版日期:2019-07-10 发布日期:2019-07-10
• 通讯作者: 李丹（1985—），女，河南通许人，副研究员，研究方向为农业遥感，（E-mail）lidan@gdas.ac.cn。
• 作者简介:周慧（1995—），女，湖南郴州人，硕士研究生，研究方向为农业遥感，（E-mail）1540206331@qq.com；
• 基金资助:

广东省科学院发展专项资金项目（2019GDASYL-0503001，2018GDASCX-0905）；广东省农业厅省级农业科技创新及推广项目（2019KJ02）

### Prediction of Nutrient Content in Litchi Leaves by UAV Multispectral Parameters

Zhou Hui1,2, Su Youyong1, Wang Chongyang2, Chen Jinyue2, Zhao Jing1,2, Jiang hao2, Chen Shuisen2 and Li Dan2

1. (1. School of Agriculture and Food, Kunming University of Science and Technology, Kunming 650000, China; 2. Guangzhou Institute of Geography// Guangdong Open Laboratory of Geographical Information System//Key Lab of Guangdong of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China)
• Online:2019-07-10 Published:2019-07-10

Abstract:

Unmanned Aerial Vehicle Remote Sensing (UAVRS) has the features of real time, flexibility, and low cost. It has been extensively used in monitoring crop nutrition and precision agricultural management. Litchi is a tropical fruit with the largest planting area, the most distinctive variety characteristics, the most obvious regional advantages, and the longest planting history in South China. Its yields are low and unstable. Nutrition diagnosis and fertilization technology as the main factors restricting the yields and quality of litchi have been research hotspots of agricultural precision management. Using litchi in the flower bud differentiation stage as an example, litchi orchards in Huizhou City, Guangdong Province, were monitored using a UAV with a Parrot Sequoia multispectral camera, and the spatial distribution differences of nutrient content in the canopy leaves were analyzed. Eighteen spectral variables were selected for the study, and the spectral variables and leaf nutrient mass fraction were analyzed by correlation analysis. A regression equation between the vegetation index and nutrient mass fraction was established by screening the correlation relationship between leaf nutrient mass fraction and vegetation index with significant and high correlations. The stability of an Residual Prediction Deviation (RPD) comprehensive evaluation model with determination coefficient, cross-validation determination coefficient, and prediction residual, as well as the relationship between the leaf nutrient mass fraction and vegetation index in different canopy layers of litchi were all discussed. The system and its influence on a UAV multi-spectral remote sensing monitoring model were examined. The results revealed the following. 1) The leaf nitrogen and potassium contents in different canopy layers of litchi increased significantly with a decrease in canopy height, particularly the spatial distribution of potassium. 2) The nitrogen mass fraction of the upper and middle leaves of the canopy was significantly correlated with the Carotenoid Reflectance Index (CRI) index as calculated by orthophoto data. The correlation between the nitrogen mass fraction in the upper leaves and CRI was significant (r=0.86, p<0.01). The potassium mass fraction in the middle and lower leaves of the canopy was significantly correlated with the multispectral parameters, namely, Normalized Green (NG) and Normalized Near Infrared (NNIR), of orthophoto data derived from the UAV. Of these parameters, the middle potassium mass fraction was significantly correlated with the spectral variable NNIR (r=-0.80, p<0.01) and the lower leaf mass fraction had the highest correlation with the NG index (r=-0.83, p<0.01). This indicated that the UAV multispectral data had the potential to estimate the changes in nitrogen and potassium mass fraction in litchi leaves of different layers. 3) The vegetation index CRI could effectively retrieve the nitrogen quality fraction of litchi upper and middle layers. The upper layer nitrogen model R2, and RPD were 0.74, 0.57, and 1.50, respectively. The middle layer nitrogen model R2R2CV and RPD were 0.64, 0.44, and 1.40, respectively. NNIR, NG, and other spectral parameters could better retrieve the potassium quality fraction of litchi upper and lower layers, where the upper layer nitrogen model R2R2CV and RPD were 0.64, 0.44, and 1.40, respectively. The potassium model R2R2CV and RPD in the middle layer were 0.79, 0.56 and 1.50, respectively. The potassium model R2R2CV and RPD in the lower layer were 0.69, 0.55, and 1.70, respectively. The model accuracy of the nutrient mass fraction in the full canopy was also higher (R2> 0.70,R2CV > 0.50, and RPD > 1.4), indicating that the spatial variation of the nutrient mass fraction in the litchi canopy leaves was based on vertical remote sensing data. The accuracy of the model for estimating the leaf nutrient content was affected. The application of UAV multi-spectral remote sensing image data can monitor litchi nutrient content very well and provide information support for precise fertilization management of litchi orchards, which has important research significance and application value.