TeaDiseaseNet: multi-scale self-attentive tea disease detection.

Abstract

Accurate detection of tea diseases is essential for optimizing tea yield and quality, improving production, and minimizing economic losses. In this paper, we introduce TeaDiseaseNet, a novel disease detection method designed to address the challenges in tea disease detection, such as variability in disease scales and dense, obscuring disease patterns. TeaDiseaseNet utilizes a multi-scale self-attention mechanism to enhance disease detection performance. Specifically, it incorporates a CNN-based module for extracting features at multiple scales, effectively capturing localized information such as texture and edges. This approach enables a comprehensive representation of tea images. Additionally, a self-attention module captures global dependencies among pixels, facilitating effective interaction between global information and local features. Furthermore, we integrate a channel attention mechanism, which selectively weighs and combines the multi-scale features, eliminating redundant information and enabling precise localization and recognition of tea disease information across diverse scales and complex backgrounds. Extensive comparative experiments and ablation studies validate the effectiveness of the proposed method, demonstrating superior detection results in scenarios characterized by complex backgrounds and varying disease scales. The presented method provides valuable insights for intelligent tea disease diagnosis, with significant potential for improving tea disease management and production.

Plant phenotyping relevance

茶葉画像から病害状態を推定する新規画像解析手法を開発し、比較実験とアブレーションで検証しており、植物病害フェノタイピングが中心です。

Code and data availability

The article's data availability statement provides a public link to the raw tea disease dataset (776 annotated images used for TeaDiseaseNet training/evaluation). The other allowed URL is a cited prior-work reference, not a paper-specific asset.