LeafMamba: a novel IoT-integrated network for accurate and efficient plant leaf disease detection

Plant leaf disease detection plays a crucial role in agricultural production and food safety. With the rapid development of the Internet of Things (IoT) and advancements in sensor technology, plant leaf disease detection has become capable of remote and real-time monitoring, greatly enhancing the efficiency and precision of agricultural production. However, current detection methods face issues of insufficient accuracy and high model complexity, limiting their effectiveness and scalability in practical applications. To address these challenges, this paper proposes a novel network architecture, LeafMamba, which integrates the core ideas of the Mamba structure and overcomes the limitations of Transformer in complex scenarios. Through the innovatively designed LF-Mamba structure, LeafMamba efficiently captures multi-scale information and progressively enhances the spatial resolution of feature maps in the decoder, thereby improving the model's ability to extract complex disease features. Experimental results on the Plant Pathology 2020 - FGVC7 and Plant Pathology 2021 - FGVC8 datasets show that LeafMamba achieves performance of 89.5% and 92.5% in mAP@0.5, respectively, significantly outperforming existing methods. Furthermore, the model reduces the number of parameters substantially, demonstrating higher computational efficiency.

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