ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images.

Abstract

The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. Firstly, we introduced the EMAGE (Efficient Multi-Scale Attention with Grouping and Expansion) module, which integrates the global distribution features and local texture details of diseased leaves in drone imagery through a multi-scale grouped attention mechanism, effectively mitigating the interference of complex background noise on feature extraction. Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model's robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. The results show that the ED-Swin Transformer model achieved excellent performance across five evaluation metrics, with scores of 94.32%, 94.56%, 98.56%, 89.22%, and 96.52%, representing improvements of 1.28%, 2.32%, 0.38%, 3.12%, and 1.4%, respectively. These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks.

Plant phenotyping relevance

ドローン画像からキャッサバ葉の病害状態を分類する新規深層学習モデルを開発し、複雑背景や病斑形状への頑健性を評価しており、植物病害表現型の取得・推定が中心である。

Code and data availability

The paper's custom cassava UAV disease dataset is explicitly restricted: 'available upon request from the corresponding author. Due to project confidentiality, the dataset cannot be disclosed at this time.' No public code, model checkpoints, or dataset URLs are provided; PlantVillage is a generic cited benchmark, not a

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