Smartphone lidar reliably estimates leaf nitrogen concentration and shoot biomass on leafy vegetable crops

URI
Thumbnail Image
Type
Conference paper
Citation
Harikumar, A., Shenhar, I., Pebes-Trujillo, M. R., Lin, Q., Moshelion, M., He, J., Ng, K. W., Gavish, M., & Herrmann, I. (2024). Smartphone lidar reliably estimates leaf nitrogen concentration and shoot biomass on leafy vegetable crops. In IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 4183-4186). https://doi.org/10.1109/igarss53475.2024.10642944
Author
Harikumar, Aravind
•
Shenhar, Itamar
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Pebes-Trujillo, Miguel R.
•
Lin, Qin
•
Moshelion, Menachem
•
He, Jie 
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Ng, Kee Woei
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Gavish, Matan
•
Herrmann, Ittai
Abstract
Leafy vegetables have a huge demand in subtropical global markets for its high nutritional value and low cost. Accurate estimation of traits like leaf nitrogen concentration and shoot biomass is critical for optimal fertilizer dosing, nutrient content assessment as well as phenotyping studies. Hand-held smartphone with high-density Light Detection and Ranging (LiDAR) systems capture huge amounts of three-dimensional (3D) plant structural and intensity information that can be used to estimate plant traits. Thus, we propose a semiautomatic proximal sensing approach to model plant nitrogen content and shoot biomass using the structural and intensity information acquired by a smartphone based LiDAR sensor. The performance of the models in estimating leaf nitrogen concentration and shoot dry-weight biomass was quantified on Chinese broccoli (Brassica oleracea) plants with prior knowledge of nitrogen dossing concentrations, and produced a minimal root mean squared error of 8.75 mg nitrogen per gram of dry weight, and 2.38 g, respectively. The potential of the proposed modelling approach to accurately estimate leaf nitrogen concentration and shoot dry-weight biomass, from smartphone LiDAR data is proven.
Date Issued
2024
ISBN
9798350360325 (online)
Publisher
IEEE
DOI
10.1109/igarss53475.2024.10642944