A multi-source domain feature adaptation network for potato disease recognition in field environment.

Abstract

Accurate identification of potato diseases is crucial for reducing yield losses. To address the issue of low recognition accuracy caused by the mismatch between target domain and source domain due to insufficient samples, the effectiveness of Multi-Source Unsupervised Domain Adaptation (MUDA) method in disease identification is explored. A Multi-Source Domain Feature Adaptation Network (MDFAN) is proposed, employing a two-stage alignment strategy. This method first aligns the distribution of each source-target domain pair within multiple specific feature spaces. In this process, multi-representation extraction and subdomain alignment techniques are utilized to further improve alignment performance. Secondly, classifier outputs are aligned by leveraging decision boundaries within specific domains. Taking into account variations in lighting during image acquisition, a dataset comprising field potato disease images with five distinct disease types is created, followed by comprehensive transfer experiments. In the corresponding transfer tasks, MDFAN achieves an average classification accuracy of 92.11% with two source domains and 93.02% with three source domains, outperforming all other methods. These results not only demonstrate the effectiveness of MUDA but also highlight the robustness of MDFAN to changes in lighting conditions.

Plant phenotyping relevance

ジャガイモ病害を画像から認識するドメイン適応ネットワークを開発し、圃場画像データセットと転移実験で性能を検証しており、植物の病害状態の表現型取得が中心である。

Code and data availability

The paper introduces the DIF_light_intensities potato disease image dataset and the MDFAN model, but no block contains any public deposit, availability statement, URL, or code release for the dataset, images, or analysis code. No qualifying paper-specific public asset is present.

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