Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
Source abstract
Abstract
Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model (R2 = 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
Funding
Faculty of Agricultural Equipment of Jiangsu University · Priority Academic Program Development of Jiangsu Higher Education Institutions
References (30)
Show source-provided references
1. Dynamic game in agriculture and industry cross-sectoral water pollution governance in developing countries10.1016/j.agwat.2020.106417 ↗
2. Progress in Research on Ecological Toxicity of Combined Pollution of Pesticide and Heavy Metals
3. Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables10.1016/j.foodchem.2023.137469 ↗
4. Vegetation Detection Based on Spectral Information and Development of a Low-cost Vegetation Sensor for Selective Spraying10.1002/ps.6874 ↗
5. Ji, X., Wang, A., and Wei, X. (2021). Precision Control of Spraying Quantity Based on Linear Active Disturbance Rejection Control Method. Agriculture, 11.10.3390/agriculture11080761 ↗
6. Research Progress of Orchard Plant Protection Mechanization Technology and Equipment in China
7. Canopy deposition characteristics of different orchard pesticide dose models
8. Liu, J., Abbas, I., and Noor, R.S. (2021). Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds Control in Strawberry Crop. Agronomy, 11.10.3390/agronomy11081480 ↗
9. Zhang, C.F., Zhai, C.Y., Zhang, M., Zhang, C., Zou, W., and Zhao, C. (2024). Staggered-Phase Spray Control: A Method for Eliminating the Inhomogeneity of Deposition in Low-Frequency Pulse-Width Modulation (PWM) Variable Spray. Agriculture, 14.10.3390/agriculture14030465 ↗
10. Estimating Leaf Area Density of Osmanthus Trees Using Ultrasonic Sensing10.1016/j.biosystemseng.2019.06.020 ↗
11. Estimation of the Leaf Area Density Distribution of Individual Trees Using High-resolution and Multi-return Airborne LiDAR Data10.1016/j.rse.2015.05.001 ↗
12. Wang, J., Zhang, Y., and Gu, R. (2020). Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction. Agriculture, 10.10.3390/agriculture10100462 ↗
13. Real time canopy density estimation using ultrasonic envelope signals in the orchard and vineyard10.1016/j.compag.2015.05.014 ↗
14. Zhou, H.T., Jia, W.D., Li, Y., and Ou, M.X. (2021). Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology. Agriculture, 11.10.3390/agriculture11101011 ↗
15. Ou, M., Hu, T., Hu, M., Yang, S., Jia, W., Wang, M., Jiang, L., Wang, X., and Dong, X. (2022). Experiment of Canopy Leaf Area Density Estimation Method Based on Ultrasonic Echo Signal. Agriculture, 12.10.3390/agriculture12101569 ↗
16. Development of Variable-rate Spraying System for High Clearance Wide Boom Sprayer Based on Li DAR Scanning
17. Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner10.1016/j.biosystemseng.2019.08.017 ↗
18. An Algorithm to Automate the Filtering and Classifying of 2D LiDAR Data for Site-specific Estimations of Canopy Height and Width in Vineyards10.1016/j.biosystemseng.2020.10.016 ↗
19. Semantic Segmentation of Sparse 3D Point Cloud Based on Geometrical Features for Trellis-structured Apple Orchard10.1016/j.biosystemseng.2020.05.015 ↗
20. A Laser Scanner Based Measurement System for Quantification of Citrus Tree Geometric Characteristics10.13031/2013.28846 ↗
21. Calculation Method of Leaf Area Density Based on Three-dimensional Laser Point Cloud
22. Total Leaf Area Estimation Based on the Total Grid Area Measured Using Mobile Laser Scanning10.1016/j.compag.2022.107503 ↗
23. Application and Experiments of the Atomization Mesh Used on the Plant Protection Spraying in Orchard
24. Development of Real-time Laser-scanning System to Detect Tree Canopy Characteristics for Variable-rate Pesticide Application
25. System Design for Peanut Canopy Height Information Acquisition Based on LiDAR
26. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications10.1016/j.compag.2021.106053 ↗
27. Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications10.1016/j.atech.2022.100153 ↗
28. Pu, Y.H., Xu, D.D., Wang, H.B., Li, X., and Xu, X. (2023). A New Strategy for Individual Tree Detection and Segmentation from Leaf-on and Leaf-off UAV-LiDAR Point Clouds Based on Automatic Detection of Seed Points. Remote Sens., 15.10.3390/rs15061619 ↗
29. Leaf area index estimation in maize and soybean using UAV LiDAR data10.1007/s11119-024-10146-9 ↗
30. Design and testing of an ultrasound system for targeted spraying in orchards10.5545/sv-jme.2011.015 ↗