Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

Hui Deng, Wenjiang Zhang (), Xiaoqian Zheng and Houxi Zhang
Additional contact information
Hui Deng: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Wenjiang Zhang: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Xiaoqian Zheng: School of Finance, Fujian Business University, Fuzhou 350016, China
Houxi Zhang: Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350028, China

Agriculture, 2024, vol. 14, issue 4, 1-17

Abstract: The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.

Keywords: crop classification; random forest (RF); segmentation algorithm; unmanned aerial vehicle (UAV); multispectral images (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/2077-0472/14/4/548/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/4/548/ (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:4:p:548-:d:1367129

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 ().