Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Abstract

Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40°and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CI red-edge ), Green band Chlorophyll Index (CI green ), Modified Normalized Difference Vegetation Index with a blue band (mND blue ) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CI green for LNC from a VZA of -60°(R 2 = 0.71, RMSE = 0.34%) and PNC from a VZA of -40°(R 2 = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0°i mage, the accuracy of PNC estimation was substantially improved (CI red-edge : R 2 = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based Frontiers in Plant Science | www.frontiersin.org

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

“…To monitor the crop growth indicators, several researchers have used UAVs equipped with hyperspectral cameras, multispectral cameras, and lidars [14,[45][46][47]. Although such monitoring reflected the growth status of the vegetation, it was not easy to popularize and apply due to its high cost.…”

“…RS based on the UAV technology has great potential for monitoring rice LDF, LAI, and leaf nitrogen accumulation [13]. Some studies have used UAVs equipped with multispectral cameras to assess the crop growth status and estimate yield, indicating that the application of UAVs to crop monitoring had the advantages of timeliness, high resolution [14][15][16], and multiband index combination [17]; however, high resolution RS is very expensive for the small scale farmers, and only big farmers, which represent only a small percent of the farmers worldwide, get benefits from its use [18]. Therefore, using low-cost UAVs to obtain RGB images to monitor crop-growth indicators and exploring the feasibility of this application in agriculture is an important research direction [2,19].…”

Effect of Different Combinations of Phosphorus and Nitrogen Fertilization on Arbuscular Mycorrhizal Fungi and Aphids in Wheat

“…Nitrogen is an essential macronutrient that promotes plant development and crop yield and quality [27][28][29][30]. Many studies have claimed that N is an essential element involved in the biosynthesis of chlorophyll [31], nucleic acids [32], amino acids [33], and enzymes [34], which are associated with the accumulation of plant nutrients [35,36]. In addition, N is the key factor for the metabolism of plant secondary chemicals, such as phytohormones (e.g., abscisic acid, ABA; indole-3-acetic acid, IAA; cytokinin, CK) [37,38], polyphenolics, tannins, and flavonoids [39][40][41][42].…”

Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images

“…Recall that Model IV included an additional explanatory variable, namely the camera azimuth angle from the plot center to the camera, could be proved to improve model fit. We observed that recent studies have confirmed that the reflectance observed by UAS are affected by multiple solar angles (Assmann et al, 2018) and camera view angles (Cheng et al, 2019). Compared to previous research, the multispectral images were not collected by the UAS at multiple discrete camera view angles intentionally in this study.…”