Abstract
The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.
Highlights
Remote sensing of agricultural fields is important to assist its management through a low-cost and non-destructive approach
We propose a new framework to infer nitrogen content in citrus-tree at a canopy level using spectral vegetation indices calculated from unmanned aerial vehicle (UAV)-imagery and the random forests (RF) algorithm
We evaluated the performance of the RF algorithm associated with spectral indices and compared it with other machine learning algorithms
Summary
Remote sensing of agricultural fields is important to assist its management through a low-cost and non-destructive approach. Algorithms like artificial neural networks (ANN), support vector machine (SVM), decision trees (DT), random forests (RF), and others are powerful tools in assisting in UAV-based image analysis [19] These algorithms performed quite well in current approaches involving plant conditions such as nutritional status [20], water-quantity [21], biomass [19], and chlorophyll content [22]. To estimate N2 in plants, many studies evaluated the potential of spectral vegetation indices in crops such as wheat, maize, rice, corn, and others [12,23,24,25,26] They can be applied at different scales, such as leaf or canopy level [27] and mitigate anisotropy effects, background shadows, and soil brightness contributions [27,28,29].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
