A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques

Yuzhe Bai, Fengjun Hou, Xinyuan Fan, Weifan Lin, Jinghan Lu, Junyu Zhou, Dongchen Fan and Lin Li ()
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Yuzhe Bai: China Agricultural University, Beijing 100083, China
Fengjun Hou: China Agricultural University, Beijing 100083, China
Xinyuan Fan: China Agricultural University, Beijing 100083, China
Weifan Lin: China Agricultural University, Beijing 100083, China
Jinghan Lu: China Agricultural University, Beijing 100083, China
Junyu Zhou: China Agricultural University, Beijing 100083, China
Dongchen Fan: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Lin Li: China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 9, 1-23

Abstract: With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on Transformer and super-resolution sampling techniques is introduced, aiming to enhance identification accuracy under challenging conditions. The Transformer model was found to effectively capture spatial dependencies in images, while the super-resolution sampling technique was employed to restore image details for subsequent identification processes. The experimental results demonstrated that this approach exhibited significant advantages across various pest image datasets, achieving Precision, Recall, mAP, and FPS scores of 0.97, 0.95, 0.95, and 57, respectively. Especially in the presence of low resolution and noise, this method was capable of performing pest identification with high accuracy. Furthermore, an adaptive optimizer was incorporated to enhance model convergence and performance. Overall, this study offers an efficient and accurate method for pest detection and identification in practical applications, holding significant practical value.

Keywords: smart agriculture; pest detection; Transformer; super resolution (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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