Next Article in Journal
Motion Branch Transformation of 3(Rc)PU Parallel Mechanism with Reconfigurable Joint and Kinematic Performance Index
Previous Article in Journal
Innovative Implementation of Computational Fluid Dynamics in Proteins Denaturation Process Prediction in Goose Breast Meat and Heat Treatment Processes Optimization
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 14
Issue 13
10.3390/app14135568
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm

by
Yuxue Wang
,
Hao Dong
*,
Songyu Bai
,
Yang Yu
and
Qingwei Duan
College of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(13), 5568; https://doi.org/10.3390/app14135568
Submission received: 22 May 2024 / Revised: 23 June 2024 / Accepted: 24 June 2024 / Published: 26 June 2024
Browse Figures
Review Reports Versions Notes

Abstract

In order to rapidly detect pest types in farmland and mitigate their adverse effects on agricultural production, we proposed an improved Yolox-tiny-based target detection method for farmland pests. This method enhances the detection accuracy of farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). In the experiments, images of pests common to seven types of farmland and particularly harmful to crops were processed through the original Yolox-tiny model after preprocessing and partial target expansion for comparative training and testing. The results indicate that the improved Yolox-tiny model increased the average precision by 7.18%, from 63.55% to 70.73%, demonstrating enhanced precision in detecting farmland pest targets compared to the original model.
Keywords: target detection; farmland pests; convolution block attention mechanism; convolutional neural network target detection; farmland pests; convolution block attention mechanism; convolutional neural network

Share and Cite

MDPI and ACS Style

Wang, Y.; Dong, H.; Bai, S.; Yu, Y.; Duan, Q. Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm. Appl. Sci. 2024, 14, 5568. https://doi.org/10.3390/app14135568

AMA Style

Wang Y, Dong H, Bai S, Yu Y, Duan Q. Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm. Applied Sciences. 2024; 14(13):5568. https://doi.org/10.3390/app14135568

Chicago/Turabian Style

Wang, Yuxue, Hao Dong, Songyu Bai, Yang Yu, and Qingwei Duan. 2024. "Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm" Applied Sciences 14, no. 13: 5568. https://doi.org/10.3390/app14135568

APA Style

Wang, Y., Dong, H., Bai, S., Yu, Y., & Duan, Q. (2024). Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm. Applied Sciences, 14(13), 5568. https://doi.org/10.3390/app14135568

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop