Smartphone Lidar Reliably Estimates Leaf Nitrogen Concentration and Shoot Biomass on Leafy Vegetable Crops

Aravind Harikumar, Itamar Shenhar, Miguel R. Pebes-Trujillo, Qin Lin, Menachem Moshelion, Jie He, Kee Woei Ng, Matan Gavish, Ittai Herrmann

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

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.

Original languageEnglish
Pages4183-4186
Number of pages4
DOIs
StatePublished - 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Precision Agriculture
  • Smart Fertigation

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