Research on the Corn Stover Image Segmentation Method via an Unmanned Aerial Vehicle (UAV) and Improved U-Net Network

Xiuying Xu, Yingying Gao, Changhao Fu, Jinkai Qiu and Wei Zhang ()
Additional contact information
Xiuying Xu: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Yingying Gao: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Changhao Fu: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Jinkai Qiu: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Wei Zhang: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China

Agriculture, 2024, vol. 14, issue 2, 1-20

Abstract: The cover of corn stover has a significant effect on the emergence and growth of soybean seedlings. Detecting corn stover covers is crucial for assessing the extent of no-till farming and determining subsidies for stover return; however, challenges such as complex backgrounds, lighting conditions, and camera angles hinder the detection of corn stover coverage. To address these issues, this study focuses on corn stover and proposes an innovative method with which to extract corn stalks in the field, operating an unmanned aerial vehicle (UAV) platform and a U-Net model. This method combines semantic segmentation principles with image detection techniques to form an encoder–decoder network structure. The model utilizes transfer learning by replacing the encoder with the first five layers of the VGG19 network to extract essential features from stalk images. Additionally, it incorporates a concurrent bilinear attention module (CBAM) convolutional attention mechanism to improve segmentation performance for intricate edges of broken stalks. A U-Net-based semantic segmentation model was constructed specifically for extracting field corn stalks. The study also explores how different data sizes affect stalk segmentation results. Experimental results prove that our algorithm achieves 93.87% accuracy in segmenting and extracting corn stalks from images with complex backgrounds, outperforming U-Net, SegNet, and ResNet models. These findings indicate that our new algorithm effectively segments corn stalks in fields with intricate backgrounds, providing a technical reference for detecting stalk cover in not only corn but also other crops.

Keywords: corn stover; UAV; semantic segmentation; U-Net model; attention mechanism (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 complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/2/217/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/2/217/ (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:217-:d:1328523

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 ().