A 3D–2D Dual-Modal Collaborative Framework Based on UAV Oblique Photogrammetry for Automated Measurement of Canopy Volume and Porosity in Banana Plantations

Abstract

Banana plant canopies exhibit pronounced three-dimensional heterogeneity due to their large, sparse, and overlapping leaves. Under dense planting and severe occlusion, conventional single-modality approaches commonly suffer from insufficient information and limited recognition accuracy, highlighting the necessity of cross-modal complementarity and collaborative modeling. This study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations. The framework integrates a task-oriented YOLO-SPES model to improve the detection of irregular and overlapping canopies and combines it with the SoftGroup instance segmentation model for three-dimensional structural extraction. At the data level, a cross-modal coordinate interaction strategy (PCI-LLCM) is introduced to achieve precise alignment between point clouds and orthomosaics. At the structural level, a consistent indexing scheme between 3D instances and 2D detection boxes is established, upon which a 3D-2D collaborative modeling algorithm (SGP-YS DMCA) and a multi-scale volume differencing algorithm (MSVDA) are developed for canopy volume and porosity estimation. At the decision level, an adaptive canopy volume completion module (CVC-AOML) leverages 2D detection information to correct and supplement errors and omissions in 3D segmentation, thereby ensuring the accuracy and completeness of large-scale automated measurements. In addition, the coupling performance of multiple geometric algorithms and collaborative models is systematically evaluated. Experimental results demonstrate that the proposed dual-modal collaborative framework achieves a coefficient of determination (R$^{2}$) of 0.885 for canopy volume estimation, representing an average improvement of 0.15 over single-modality baseline methods, with a corresponding mean absolute percentage error (MAPE) of 6.4%. For canopy porosity estimation, an R$^{2}$of 0.65 is obtained with a MAPE of 1.05%. These results not only overcome the limitations of single-modality approaches in phenotypic analysis of complex banana canopies but also provide a low-cost and scalable solution for large-scale agricultural monitoring and precision management of tropical fruit crops.

Plant phenotyping relevance

UAV画像・点群を用いてバナナの樹冠体積と多孔性という明示的な植物形質を自動推定する3D–2D手法を開発し、ベースライン比較と精度評価を行っているため、フェノタイピング手法が中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.