Alleviating labeled data scarcity: a lightweight semi-supervised network for Moso bamboo age determination

Abstract

Accurate determination of Moso bamboo ( Phyllostachys edulis ) age is a critical task for efficient and sustainable bamboo forest management. However, existing methods face significant challenges: traditional manual assessment is subjective and labor-intensive, while advanced technologies like LiDAR are prohibitively expensive for widespread application. Furthermore, high-performance deep learning models, which offer a promising alternative, typically rely on large-scale labeled datasets, a resource that is particularly scarce and costly to acquire in the field of Moso bamboo. To address these limitations, we propose a lightweight, semi-supervised framework, the Dual-Color-Texture Moso Bamboo Age Decoupling Network (DCT-MBADNet). Our framework first leverages the Segment Anything Model (SAM) to isolate the bamboo culm, effectively eliminating complex background interference. A novel dual-stream feature decoupling module is then introduced to independently extract color degradation and texture evolution features, which are biologically significant indicators of bamboo age. A dynamic gating mechanism is employed to adaptively fuse these features. Simultaneously, we integrate an age-dependent dynamic threshold strategy within a Mean Teacher semi-supervised framework to synergistically utilize a small set of labeled data and a large volume of unlabeled data, thereby enhancing pseudo-label quality and model generalization. Experimental results demonstrate that our semi-supervised DCT-MBADNet achieves a test set accuracy of 89.6%, representing a 4.5% improvement over its fully supervised baseline. With a minimal parameter count of just 1.6 M, the proposed model provides a low-cost, robust, and deployable solution for precise Moso bamboo management and offers a novel paradigm for plant phenotyping analysis under data-scarce conditions.

Plant phenotyping relevance

竹稈画像から色・テクスチャ特徴を抽出して齢という植物形質を推定する半教師あり手法を開発しており、モデル構築と性能評価が研究の中心である。

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