Adaptive loss-guided multi-stage residual ASPP for lesion segmentation and disease detection in cucumber under complex backgrounds.

Abstract

Background In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. Results An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. Conclusions The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.

Plant phenotyping relevance

キュウリ葉の病斑を画像から抽出・分割する手法の開発が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。

Code and data availability

The paper's cucumber disease image datasets (Xinjiang Institute of Technology dataset and the extensive cucumber dataset) and pixel-level annotations are not publicly available; the authors state raw data cannot be shared due to confidentiality but processed data may be requested from the corresponding author. No code,

No evidence-backed public reproduction asset is currently recorded.