P2OP—Plant Pathology on Palms: A deep learning-based mobile solution for in-field plant disease detection

Abstract

Plant diseases are one of the dominant factors that threaten sustainable agriculture, leading to economic losses. Developing an accurate mobile-based plant disease detection methodology is important for enabling rapid identification of emerging diseases directly from the farms. The deep learning methods have limited usage in mobile-based applications as they require larger memory and processing power to operate directly on smartphones or internet connectivity when used with a client–server computing model. To address this challenge, we propose a mobile-based lightweight deep learning-based model, which requires only a small footprint and processing power while maintaining higher detection accuracy. With around 0.088 billion multiply–accumulation operations, 0.26 million parameters, and 1 MB storage space, this framework achieved 97%, 97.1% and 96.4% accuracies on apple, citrus and tomato leaves datasets, respectively. One of our tiny models achieved 93.33% accuracy on a custom sourced in-the-wild apple leaves images dataset, which affirms the in-field applicability of the proposed framework. The superiority of the proposed model is further demonstrated through a comparative study with equivalent lightweight models.

Plant phenotyping relevance

植物の葉画像から病害状態を推定する軽量モバイル深層学習手法の開発・比較検証が中心であり、植物表現型（病徴・病害状態）の取得手法に該当する。

Code and data availability

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