A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants

Zhizhou Ren, Kun Liang (), Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei and Yi Zhang
Additional contact information
Zhizhou Ren: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Kun Liang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yihe Liu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Xiaoxiao Wu: Key Laboratory of Biotoxin Analysis & Assessment for State Market Regulation, Nanjing Institute of Product Quality Inspection & Testing, Nanjing 210019, China
Chi Zhang: Key Laboratory of Biotoxin Analysis & Assessment for State Market Regulation, Nanjing Institute of Product Quality Inspection & Testing, Nanjing 210019, China
Xiuming Mei: Key Laboratory of Biotoxin Analysis & Assessment for State Market Regulation, Nanjing Institute of Product Quality Inspection & Testing, Nanjing 210019, China
Yi Zhang: Jiangsu Grain and Oil Quality Monitoring Center, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 4, 1-15

Abstract: Common bunt disease in wheat is a serious threat to crops and food security. Rapid assessments of its severity are essential for effective management. The electronic nose (e-nose) system is used to capture volatile organic compounds (VOCs), particularly trimethylamine (TMA), which serves as a key marker of common bunt disease in wheat. In this paper, the GFNN (gas feature neural network) model is proposed for detecting VOCs from the e-nose system, providing a lightweight and efficient approach for assessing disease severity. Multiscale convolution is employed to extract both global and local features from gas data, and three attention mechanisms are used to focus on important features. GFNN achieves 98.76% accuracy, 98.79% precision, 98.77% recall, and an F1-score of 98.75%, with only 0.04 million parameters and 0.42 million floating-point operations per second (FLOPS). Compared to traditional and current deep learning models, GFNN demonstrates superior performance, particularly in small-sample-size scenarios. It significantly improves the deep learning performance of the model in extracting key gas features. This study offers a practical, rapid, and cost-effective method for monitoring and managing common bunt disease in wheat, enhancing crop protection and food security.

Keywords: electronic nose system; multiscale convolution; common bunt disease in wheat; attention mechanism; lightweight structure (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: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/4/415/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/4/415/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:4:p:415-:d:1592354

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().