Research on visualization of cotton canopy structure and extraction of feature parameters based on dual-perspective point cloud data

ABSTRACT

Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham’s algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients ($R^2$) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture.

KEYWORDS:

• Cotton
• remote sensing
• Airborne Laser Scanning (ALS)
• canopy gaps

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 32271985, the Basic and Applied Basic Research Foundation of Guangdong Province, China under Grant 2022A1515011008, the Scientific and Technological Planning Project of Guangzhou City, China under Grant 2024B03J1268 and 2024E04J0232, and the earmarked fund for CARS (CARS-15-22).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data produced in this study will be deposited in the following github repository: ‘https://github.com/gumaijian/Cotton-experimental-data’ once the manuscript has been accepted for publication after any revisions have been completed. This data availability statement will be updated at that time and a Zenodo doi will be created for this repository so that a permanent record is available for this paper.

Additional information

Funding

The work was supported by the Basic and Applied Basic Research Foundation of Guangdong Province [2022A1515011008]; Earmarked Fund for China Agriculture Research System [CARS-15-22]; National Natural Science Foundation of China [32271985]; Scientific and Technological Planning Project of Guangzhou City [2024B03J1268,2024E04J0232].