Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism

Wei Zhang, Youqiang Sun, He Huang, Haotian Pei, Jiajia Sheng and Po Yang
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Wei Zhang: Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Youqiang Sun: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
He Huang: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Haotian Pei: Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Jiajia Sheng: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Po Yang: Department of Computer Science, Sheffield University, Sheffield S1 1DA, UK

Agriculture, 2022, vol. 12, issue 8, 1-19

Abstract: In precision agriculture, effective monitoring of corn pest regions is crucial to developing early scientific prevention strategies and reducing yield losses. However, complex backgrounds and small objects in real farmland bring challenges to accurate detection. In this paper, we propose an improved model based on YOLOv4 that uses contextual information and attention mechanism. Firstly, a context priming module with simple architecture is designed, where effective features of different layers are fused as additional context features to augment pest region feature representation. Secondly, we propose a multi-scale mixed attention mechanism (MSMAM) with more focus on pest regions and reduction of noise interference. Finally, the mixed attention feature-fusion module (MAFF) with MSMAM as the kernel is applied to selectively fuse effective information from additional features of different scales and alleviate the inconsistencies in their fusion. Experimental results show that the improved model performs better in different growth cycles and backgrounds of corn, such as corn in vegetative 12th, the vegetative tasseling stage, and the overall dataset. Compared with the baseline model (YOLOv4), our model achieves better average precision (AP) by 6.23%, 6.08%, and 7.2%, respectively. In addition, several comparative experiments were conducted on datasets with different corn growth cycles and backgrounds, and the results verified the effectiveness and usability of the proposed method for such tasks, providing technical reference and theoretical research for the automatic identification and control of pests.

Keywords: early pest control; pest region; small object; context; attention mechanism; feature fusion (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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