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Article

SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture

by
Mingxia Liang
1,2,
Longpeng Ding
1,2,
Jiangchun Chen
1,2,
Liming Xu
3,
Xinjie Wang
4,
Jingbin Li
1,2,* and
Hongfei Yang
1,2,5,*
1
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
2
Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
3
Mechanical Engineering and Power Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
4
College of Economics and Management, Shihezi University, Shihezi 832099, China
5
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
*
Authors to whom correspondence should be addressed.
Agriculture 2024, 14(10), 1760; https://doi.org/10.3390/agriculture14101760
Submission received: 5 September 2024 / Revised: 27 September 2024 / Accepted: 29 September 2024 / Published: 5 October 2024
(This article belongs to the Section Digital Agriculture)
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Abstract

Identifying drivable areas between orchard rows is crucial for intelligent agricultural equipment. However, challenges remain in this field’s accuracy, real-time performance, and generalization of deep learning models. This study proposed the SwinLabNet model in the context of jujube orchards, an innovative network model that utilized a lightweight CNN-transformer hybrid architecture. This approach optimized feature extraction and contextual information capture, effectively addressing long-range dependencies, global information acquisition, and detailed boundary processing. After training on the jujube orchard dataset, the SwinLabNet model demonstrated significant performance advantages: training accuracy reached 97.24%, the mean Intersection over Union (IoU) was 95.73%, and the recall rate was as high as 98.36%. Furthermore, the model performed exceptionally well on vegetable datasets, highlighting its generalization capability across different crop environments. This study successfully applied the SwinLabNet model in orchard environments, providing essential support for developing intelligent agricultural equipment, advancing the identification of drivable areas between rows, and laying a solid foundation for promoting and applying intelligent agrarian technologies.
Keywords: drivable area identification; SwinLabNet network model; jujube orchard environment; agricultural Intelligence drivable area identification; SwinLabNet network model; jujube orchard environment; agricultural Intelligence

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MDPI and ACS Style

Liang, M.; Ding, L.; Chen, J.; Xu, L.; Wang, X.; Li, J.; Yang, H. SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture. Agriculture 2024, 14, 1760. https://doi.org/10.3390/agriculture14101760

AMA Style

Liang M, Ding L, Chen J, Xu L, Wang X, Li J, Yang H. SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture. Agriculture. 2024; 14(10):1760. https://doi.org/10.3390/agriculture14101760

Chicago/Turabian Style

Liang, Mingxia, Longpeng Ding, Jiangchun Chen, Liming Xu, Xinjie Wang, Jingbin Li, and Hongfei Yang. 2024. "SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture" Agriculture 14, no. 10: 1760. https://doi.org/10.3390/agriculture14101760

APA Style

Liang, M., Ding, L., Chen, J., Xu, L., Wang, X., Li, J., & Yang, H. (2024). SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture. Agriculture, 14(10), 1760. https://doi.org/10.3390/agriculture14101760

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