EconPapers    
Economics at your fingertips  
 

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage ( Brassica rapa subsp. Pekinensis ) Plants

Xiandan Du, Zhongfa Zhou () and Denghong Huang
Additional contact information
Xiandan Du: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Zhongfa Zhou: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Denghong Huang: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, China

Agriculture, 2024, vol. 14, issue 11, 1-18

Abstract: The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage ( Brassica rapa subsp. Pekinensis ) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Keywords: UAV visible light images; frequency domain filtering; Otsu; morphological filtering; spatial scale effect; accurate identification (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/11/1871/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/11/1871/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:11:p:1871-:d:1505137

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1871-:d:1505137