GACNet: A Geometric and Attribute Co-Evolutionary Network for Citrus Tree Height Extraction From UAV Photogrammetry-Derived Data

Abstract

The undulating terrain and complex backgrounds of citrus plantations introduce nonlinear variations that significantly impede the high-precision estimation of citrus tree heights from remote sensing data. To overcome these obstacles, we introduce a novel geometric and attribute co-evolutionary network, tailored for extracting citrus tree heights using unmanned aerial vehicle photogrammetry-derived data. Our approach integrates a multisource feature interaction module with a multisource feature aggregation module, fostering the co-evolution of deep feature responses across various datasets. Notably, this includes a sophisticated triple-feature interaction mechanism that considers position, channel, and spatial correlation to enhance the aggregation of geometric features. In addition, we employ a multilevel feature aggregation decoder leveraging cross-attention, ensuring attribute context consistency and facilitating efficient tree height extraction. Quantitative analysis across datasets reveals our method's superior performance, with a 2% –7% increase in mean intersection over union for canopy segmentation and a robust correlation of 0.77 between estimated and reference tree heights, accompanied by an MAE of 0.25 m and an RMSE of 0.38 m. Comparative experiments indicate that our method outperforms current state-of-the-art networks, showing resilience to terrain undulations and offering reliable cross-region and cross-scale tree height estimation capabilities.

Plant phenotyping relevance

UAV写真測量データから柑橘樹の樹高を抽出する新規ネットワークを開発・評価しており、植物形質の取得手法が研究の中心である。

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