LeafConvNeXt: Enhancing plant disease classification for the future of unmanned farming

Abstract

With the burgeoning global population, the necessity for sustainable and efficient agricultural practices has become paramount. The primary objective of this paper is to develop an accurate and efficient deep learning model for the timely detection of plant diseases, with a focus on improving crop yield and reducing economic loss. Specifically, this study addresses diseases affecting plant leaves, which present a significant challenge to agricultural productivity. To meet this objective, we introduce LeafConvNeXt, a novel deep learning model tailored to identify plant diseases by meticulously analyzing distinctive features of infected leaves. The secondary objectives include enhancing the interpretability of the model and ensuring its adaptability in resource-constrained environments. LeafConvNeXt integrates convolutional and attention mechanisms, achieving outstanding performance with an accuracy rate exceeding 99% across 52 distinct leaf diseases, outperforming existing contemporary methods. The model’s interpretability is further improved by utilizing LayerCAM, allowing for user-friendly visualization of the diagnostic process. Additionally, its low computational demands and high adaptability make it a practical solution for diverse applications, particularly in its potential integration into intelligent agricultural systems for real-time plant disease monitoring. By emphasizing green energy utilization and regulatory compliance in the era of Artificial Intelligence, LeafConvNeXt lays the groundwork for unmanned farming and a sustainable future in agriculture.

Plant phenotyping relevance

葉画像から植物病害を識別する深層学習モデルの開発が研究の中心であり、感染植物の状態を直接推定するため、植物フェノタイピング手法に該当する。

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