ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Crop and Product Physiology

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1592329

This article is part of the Research TopicRevolutionizing Plant Phenotyping: From Single Cells to SystemsView all 3 articles

Harnessing Smartphone RGB Imagery and LiDAR Point Cloud for Enhanced Leaf Nitrogen and Shoot Biomass Assessment -Chinese Spinach as a Case Study

Provisionally accepted
  • 1Nanyang Technological University, Singapore, Singapore
  • 2Singapore-HUJ Alliance for Research and Enterprise (SHARE)., Singapore, Singapore
  • 3The Robert H. Smith Faculty of Agriculture, Food and Environment, Hebrew University of Jerusalem, Rehovot, Central District, Israel
  • 4National Institute of Education, Nanyang Technological University, Singapore, Singapore
  • 5Nanyang Environment & Water Research Institute, Nanyang Technological University, Singapore, Singapore
  • 6School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel

The final, formatted version of the article will be published soon.

Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution in-situ sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data.In this study, we propose an innovative approach to estimate leaf nitrogen concentration and shoot dry-weight biomass by integrating smartphone-based RGB imagery with Light Detection and Ranging (LiDAR) data, using Amaranthus dubius (Chinese spinach) as a case study. Specifically, we derive spectral features from the RGB images and structural features from the LiDAR data to predict these key plant parameters. Furthermore, we investigate how plant traits, modeled using smartphone data based indices, respond to varying nitrogen dosing, enabling the identification of the optimal nitrogen dosage to maximize yield in terms of shoot dry-weight biomass and vigor. The performance of crop parameter estimation was evaluated using three regression approaches: support vector regression, random forest regression, and lasso regression. The results demonstrate that combining smartphone RGB imagery with LiDAR data enables accurate estimation of leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, achieving best-case relative root mean square errors as low as 0.06, 0.15, and 0.05, respectively. This study lays the groundwork for smartphone-based estimate leaf nitrogen concentration and shoot biomass, supporting accessible precision agriculture practices.

Keywords: Biomass estimation, smartphone, lidar, modelling, Smart farming

Received: 12 Mar 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Harikumar, Shenhar, R. Pebes-Trujillo, Qin, Moshelion, HE, Ng, Gavish and Herrmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ittai Herrmann, Singapore-HUJ Alliance for Research and Enterprise (SHARE)., Singapore, Singapore

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.