RepDI: A light-weight CPU network for apple leaf disease identification

Abstract

Apple disease is one of the major factors affecting apple production, and the visual diagnosis of apple leaves is an efficient disease identification solution. In this paper, we propose an efficient lightweight model based on structural reparameterization for apple leaf disease identification, called RepDI for short. To achieve faster inference on the CPU devices, we introduce depth-wise separable convolution and structural reparameterization technology in RepDI, which has different structures during training and inference. In addition, to better capture diseased leaves and disease regions in complex contexts, we propose the parallel dilated attention mechanism module and embed it into RepDI. Experiments show that RepDI can achieve state-of-the-art performance in disease identification task, compared to most lightweight models. Meanwhile, RepDI achieves the fastest inference speed on our desktop CPU, which is an important factor in practical applications. Furthermore, we collect and annotate a novel dataset for apple leaf diseases from real scenarios, called Real-ALD, which is more challenging than previous datasets. And RepDI achieves a top-1 accuracy of 98.92 in the Real-ALD dataset under a limited training configuration. Our code is released to contribute to the plant protection community and we will further explore the potential of RepDI for down-stream detection, segmentation tasks.

Plant phenotyping relevance

リンゴ葉の病徴・病変領域を画像から識別するモデルを開発し、実画像データセットも収集・アノテーションしており、植物病害状態の表現型取得・推定が中心である。

Code and data availability

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