Transformer-based detection of abnormal rice growth using drone-based multispectral imaging

Abstract

Rice is a vital staple food for global food security and a primary income source for millions of farmers worldwide. However, abnormal rice growth poses a serious threat to both yield stability and grain quality, undermining agricultural productivity. Early detection of such anomalies is therefore essential to mitigate yield losses. However, existing methods either targeted only one symptom at a time, or failed to generalize under various field conditions. Moreover, lightweight real-time inference is needed for on-board UAV deployment, yet most high-accuracy models incur prohibitive computational cost. In this study, we propose ARG-TR model, a lightweight transformer-based semantic segmentation framework built on the SegFormer architecture, which utilizes long-range dependencies to identify complex growth anomalies. The model is trained and validated on a large-scale, drone-captured multi-spectral dataset. By integrating a hierarchical transformer encoder with a lightweight decoder, ARG-TR achieves rapid convergence during training and demonstrates strong generalization to unseen data. The experimental results on a challenging dataset of abnormal rice growth patterns show that ARG-TR achieves a robust Intersection over Union (IoU) of 64.8, which outperforms state-of-the-art baselines such as MaskFormer and KNet in both accuracy and computational efficiency.

Plant phenotyping relevance

ドローンマルチスペクトル画像からイネの異常生育状態を抽出するセマンティックセグメンテーション手法を開発・検証しており、植物状態の取得方法が中心である。

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