BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices

Abstract

High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.

Plant phenotyping relevance

スマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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