利用无人机的多光谱参数预测荔枝叶片养分质量分数

Prediction of Nutrient Content in Litchi Leaves by UAV Multispectral Parameters

以花芽分化期荔枝为例,分析了荔枝冠层叶片养分质量分数的空间分布差异;选用18种光谱变量,研究了荔枝不同冠层叶片养分质量分数与光谱变量的关系及其对无人机多光谱遥感监测模型的影响。结果表明:荔枝不同冠层叶片的氮、钾质量分数随冠层高度降低而明显提高;冠层中、上层叶片氮质量分数与无人机正射数据计算的类胡萝卜素反射指数(CRI)相关性最高(”=0.86, p < 0.01 );冠层中、下层叶片钾质量分数与无人机正射数据的光谱变量显著相关,且与标准绿波段(NG)指数的相关程度最高(”=-0.83,”<0.01)。荔枝冠层叶片养分质量分数空间变化对基于垂直观测遥感数据建立的叶片养分质量分数估算模型精度有影响,无人机多光谱数据具有估算荔枝叶片氮、钾质量分数变化的潜力,但估算精度与冠层高度有关。

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, pvO.Ol). 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,/?<0.01) and the lower leaf mass fraction had the highest correlation with the NG index (z=-0.83,/?<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 W, R^v and RPD were 0.74,0.57, and 1.50, respectively. The middle layer nitrogen model 疋,R^v 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 W, R^v and RPD were 0.64, 0.44, and 1.40, respectively. The potassium model W, R^v and RPD in the middle layer were 0.79, 0.56 and 1.50, respectively. The potassium model 7?2, 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 (7?2> 0.70,> 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.