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

Author:

Xie Boyu1,Su Qi1,Tang Beilun1,Li Yan1,Yang Zhengwu1,Wang Jiaoyang1,Wang Chenxi1,Lin Jingxian2ORCID,Li Lin1

Affiliation:

1. China Agricultural University, Beijing 100083, China

2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China

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.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

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