Combining Neural Architecture Search with Knowledge Graphs in Transformer: Advancing Chili Disease Detection

Boyu Xie, Qi Su, Beilun Tang, Yan Li, Zhengwu Yang, Jiaoyang Wang, Chenxi Wang, Jingxian Lin and Lin Li ()
Additional contact information
Boyu Xie: China Agricultural University, Beijing 100083, China
Qi Su: China Agricultural University, Beijing 100083, China
Beilun Tang: China Agricultural University, Beijing 100083, China
Yan Li: China Agricultural University, Beijing 100083, China
Zhengwu Yang: China Agricultural University, Beijing 100083, China
Jiaoyang Wang: China Agricultural University, Beijing 100083, China
Chenxi Wang: China Agricultural University, Beijing 100083, China
Jingxian Lin: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Lin Li: China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 10, 1-22

Abstract: With the advancement in modern agricultural technologies, ensuring crop health and enhancing yield have become paramount. This study aims to address potential shortcomings in the existing chili disease detection methods, particularly the absence of optimized model architecture and in-depth domain knowledge integration. By introducing a neural architecture search (NAS) and knowledge graphs, an attempt is made to bridge this gap, targeting enhanced detection accuracy and robustness. A disease detection model based on the Transformer and knowledge graphs is proposed. Upon evaluating various object detection models on edge computing platforms, it was observed that the dynamic head module surpassed the performance of the multi-head attention mechanism during data processing. The experimental results further indicated that when integrating all the data augmentation methods, the model achieved an optimal mean average precision (mAP) of 0.94. Additionally, the dynamic head module exhibited superior accuracy and recall compared to the traditional multi-head attention mechanism. In conclusion, this research offers a novel perspective and methodology for chili disease detection, with aspirations that the findings will contribute to the further advancement of modern agriculture.

Keywords: chili disease identification; knowledge graphs; Transformers; neural architecture search; focal loss (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 complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/10/2025/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/10/2025/ (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:13:y:2023:i:10:p:2025-:d:1262648

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 ().