Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images

Abstract

Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R2) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg−1, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

“…In this paper, we attempted several physical and chemical covariates to construct an SVM jujube trees' health assessment model with R 2 and RMSE of 0.467 and 0.475, respectively. The SVM model assessed accuracy decreased by 0.148 in R 2 and increased by 0.011 in RMSE compared to the health regression model constructed using SPAD value only, and this result is different from previous studies [40]. This may be due to the fact that SVMs have high requirements from the dataset [41], and SVMs deal with multidimensional data with strong generalization ability, while SVMs deal with nonlinear problems with better robustness and interpretability.…”

New Trends and Challenges in Precision and Digital Agriculture

“…These features make drones increasingly applicable to agriculture. Assessing the field germination of soybean plants [71]; determining the nutritional status of spring wheat after the earing stage [72]; and monitoring nitrogen concentrations in walnut [73], cotton [74], or phenotyping in corn breeding [75] are just a few examples of what can be achieved using UAVs and spectral cameras in modern agriculture.…”

Precision Operation Technology and Intelligent Equipment in Farmland

“…Unmanned aerial vehicles (UAVs) have the characteristics of being mobile and flexible and less affected by the terrain [63,64]. The authors of [65] used a UAV platform to detect nitrogen in walnut trees, and proposed a canopy simulation method that contains spectral information of walnut tree canopies to realize canopy nitrogen inversion. The results of this study provide a theoretical basis and method of realization of the rapid detection of nutrient composition in large fruit trees.…”