Real-Field-Ready and Digitally Sustainable Plant Disease Recognition via Federated Multimodal Edge Learning and Few-Shot Domain Adaptation

Abstract

Plant disease diagnosis in real-world agricultural environments is challenged by data scarcity, domain shift, privacy constraints, and limited edge-device resources. This paper proposes FMEL-FSDA, a Federated Multimodal Edge Learning framework with Few-Shot Domain Adaptation for robust field-based plant disease recognition. The framework integrates attention-based RGB–text feature fusion, privacy-preserving federated learning, rapid few-shot personalization, and uncertainty-aware inference within an edge-efficient architecture. Federated training enables collaborative learning across distributed farms without sharing raw data, while few-shot adaptation allows fast deployment to new regions using only 1–10 labeled samples per class. Experiments on the PlantWild in-the-wild dataset show that FMEL-FSDA outperforms centralized, federated, and few-shot baselines, achieving 93.78% accuracy, 93.33% F1-score, and 0.97 AUC. The model maintains strong performance under privacy mechanisms such as gradient perturbation and secure aggregation, reduces communication overhead by up to 4×, and supports low-latency edge inference. Uncertainty estimation and Grad-CAM-based explainability further enhance reliability by identifying low-confidence cases and highlighting disease-relevant regions. Overall, FMEL-FSDA offers a scalable, privacy-aware, and field-ready solution for intelligent plant disease diagnosis in precision agriculture.

Plant phenotyping relevance

植物病害の画像ベース認識手法を開発し、複数ベースラインとの比較評価と実フィールド性能検証を行っており、病害状態の取得・推定が研究の中心である。

Code and data availability

The supplied blocks describe the FMEL-FSDA framework and experiments on the PlantWild dataset, but contain no data or code availability statement, no authors' public repository URL, and no deposit identifiers. The PlantWild dataset is referenced as an evaluation input but no access URL is given in the supplied text. No

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