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Agronomy
Volume 13
Issue 12
10.3390/agronomy13122866
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Article

Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing

by
Jiayi Xiao
1,
Gaobi Kang
1,
Linhui Wang
2,
Yongda Lin
3,
Fanguo Zeng
1,
Jianyu Zheng
1,
Rong Zhang
1 and
Xuejun Yue
1,*
1
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
2
School of Intelligent Manufacturing, Hunan University of Science and Engineering, Yongzhou 425100, China
3
College of Science, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(12), 2866; https://doi.org/10.3390/agronomy13122866
Submission received: 7 September 2023 / Revised: 28 October 2023 / Accepted: 8 November 2023 / Published: 22 November 2023
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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Abstract

Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these challenges, this paper proposes an improved lightweight network, named YOLOv7-MGPC (YOLOv7-Mosaic-GhostNet-Pruning-CBAM), that enables real-time lychee disease detection. In this study, we collected datasets of lychee diseases, covering four types of leaf diseases, and employed Mosaic data augmentation for data preprocessing. Building upon the YOLOv7 framework, we replaced the original backbone network with the lightweight GhostNetV1 and applied channel pruning to effectively reduce the parameter overhead. Subsequently, an attention mechanism called CBAM was incorporated to enhance the detection accuracy. The resultant model was then deployed to edge devices (Nvidia Jetson Nano) for real-world applications. Our experiments showed that our enhanced YOLOv7 variant outperforms the original model by a large margin, achieving a speed increase from 120 frames/s to 217 frames/s while maintaining an accuracy of 88.6%. Furthermore, the parameter size was substantially reduced from 36.5 M to 7.8 M, which firmly demonstrates the effectiveness of our methods in enabling model deployment on edge devices for lychee disease detection.
Keywords: YOLOv7; GhostNetV1; channel pruning; disease identification; edge computing YOLOv7; GhostNetV1; channel pruning; disease identification; edge computing

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MDPI and ACS Style

Xiao, J.; Kang, G.; Wang, L.; Lin, Y.; Zeng, F.; Zheng, J.; Zhang, R.; Yue, X. Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing. Agronomy 2023, 13, 2866. https://doi.org/10.3390/agronomy13122866

AMA Style

Xiao J, Kang G, Wang L, Lin Y, Zeng F, Zheng J, Zhang R, Yue X. Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing. Agronomy. 2023; 13(12):2866. https://doi.org/10.3390/agronomy13122866

Chicago/Turabian Style

Xiao, Jiayi, Gaobi Kang, Linhui Wang, Yongda Lin, Fanguo Zeng, Jianyu Zheng, Rong Zhang, and Xuejun Yue. 2023. "Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing" Agronomy 13, no. 12: 2866. https://doi.org/10.3390/agronomy13122866

APA Style

Xiao, J., Kang, G., Wang, L., Lin, Y., Zeng, F., Zheng, J., Zhang, R., & Yue, X. (2023). Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing. Agronomy, 13(12), 2866. https://doi.org/10.3390/agronomy13122866

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