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

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.

Plant phenotyping relevance

唐辛子の病徴を画像から検出・分類するモデルの開発と評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当します。

Code and data availability

The supplied blocks describe a chili disease image dataset (collected from online agricultural databases, annotated with LabelImg) and a Transformer/NAS/knowledge-graph model, but contain no public dataset deposit, no author code/model release, and no availability statements with URLs. The only URLs are the article DOI

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