Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices

Yiliang Kang, Yang Wang, Yanmin Fan (), Hongqi Wu, Yue Zhang, Binbin Yuan, Huijun Li, Shuaishuai Wang and Zhilin Li
Additional contact information
Yiliang Kang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yang Wang: College of Grass Industry, Xinjiang Agricultural University, Urumqi 830052, China
Yanmin Fan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Hongqi Wu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yue Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Binbin Yuan: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Huijun Li: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Shuaishuai Wang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Zhilin Li: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2024, vol. 14, issue 2, 1-15

Abstract: To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R 2 ) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm −2 , and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index.

Keywords: wheat; UAV multispectral imagery; yield prediction; color index; textural features (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/2077-0472/14/2/167/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/2/167/ (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:2:p:167-:d:1324738

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 ().