基于环绕式无人车表型平台和同源传感阵列的田间原位表型数据融合解析方法

Method for Fusion Analysis of In-situ Field Phenotyping Data Based on Surrounding Unmanned Vehicle Phenotyping Platform and Homologous Sensor Arrays

摘要:

作物表型信息的高通量、精准采集与解析是现代农业育种与精准栽培技术体系的基础。然而，田间复杂环境下传统人工测量方式存在效率低、劳动强度大、主观误差高等局限，难以满足大规模、多性状、连续时序的表型获取需求。为此提出了一种基于环绕式无人车表型平台与多模态同源传感阵列的田间原位表型数据融合解析方法。该平台集成了RGB相机、深度相机、近红外和同源传感器，能够实现对作物目标的多角度、立体化原位观测。围绕田间复杂环境下的多源异构数据处理难题，设计了系统的表型信息融合流程，包括图像预处理、深度信息提取、三维重建、时序跟踪与特征解析等关键技术模块，实现了对株高、冠层结构、空间分布等核心表型特征的高精度提取与动态重构。以田间玉米为对象的实地试验表明，该平台可在不同生育时期稳定、连续地获取高质量的多模态表型数据，重建模型的株高测量结果与人工测量高度相关，平均误差控制在5cm以内，验证了方法的准确性与鲁棒性。与传统单点或机械旋转式观测方式相比，该平台具备更高的田间适应性与作业灵活性，可实现快速部署和高效作业，为大规模田间表型数据的采集和解析提供了有效技术支撑。本研究提出的环绕式无人车表型观测与多模态数据融合方法，为农作物育种和精准农业提供了一种高通量、低扰动、可扩展的田间原位表型组学技术解决方案。

Abstract:

High-throughput and precise acquisition and analysis of crop phenotypic information are fundamental components of modern agricultural breeding and precision cultivation systems. However, traditional manual measurements in complex field environments are limited by low efficiency, high labor intensity, and strong subjectivity, making it difficult to meet the growing demand for large-scale, multi-trait, and time-series phenotyping. To address these challenges, an in-field phenotypic data fusion and analysis method was proposed based on a ring-shaped unmanned vehicle phenotyping platform and a multimodal homogeneous sensor array. The platform integrated multiple homogeneous sensors, including RGB and depth cameras, enabling multi-angle and three-dimensional in situ crop observations. A systematic multi-source heterogeneous data fusion workflow was designed, consisting of image preprocessing, depth information extraction, 3D reconstruction, temporal tracking, and feature analysis, to achieve accurate extraction and dynamic reconstruction of key phenotypic traits such as plant height, canopy structure, and spatial distribution. Field experiments were conducted on maize plants at multiple growth stages. The results demonstrated that the proposed platform can stably and continuously acquire high-quality multimodal phenotypic data. The reconstructed plant height measurements showed a high correlation with manual measurements, with an average error within 5cm, verifying the accuracy and robustness of the method. Compared with conventional single-view or mechanically rotating observation methods, the proposed platform exhibited superior adaptability to field environments, allowing rapid deployment and efficient operation, thereby providing an effective technical foundation for large-scale in-field phenotyping. Furthermore, the platform’s advantages were discussed in terms of portability, scalability, timeliness, and automation, and envisions future developments toward embodied intelligence and autonomous phenotyping. The proposed ring-type unmanned vehicle platform and multimodal data fusion method can provide a high-throughput, low-disturbance, and scalable technical solution for in-field crop phenomics, supporting modern crop breeding and precision agriculture.