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Xu L, Shi X, Tang Z, He Y, Yang N, Ma W, Zheng C, Chen H, Zhou T, Huang P, Wu Z, Wang Y, Zou Z, Kang Z, Dai J, Zhao Y. ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family. Front Plant Sci 2023; 14:1176300. [PMID: 37546271 PMCID: PMC10400454 DOI: 10.3389/fpls.2023.1176300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/16/2023] [Indexed: 08/08/2023]
Abstract
Introduction Insect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy. Methods To address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters. Results Experimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%. Discussion Our model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.
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Affiliation(s)
- Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Xiaoshi Shi
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Zuoliang Tang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Ning Yang
- College of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Wei Ma
- Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China
| | - Chengyu Zheng
- Regulation Department, China Telecom Corporation Limited Sichuan Branch, Chengdu, China
| | - Huabao Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
| | - Taigang Zhou
- Changhong Digital Agriculture Research Institute, Sichuan Changhong Yunsu Information Technology Co., Ltd, Chengdu, China
| | - Peng Huang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Zhijun Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Jianwu Dai
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, China
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