Design and application of a multi-source sensor data fusion system based on a robot phenotype platform

Abstract

The compact, high-throughput phenotyping platform, characterized by its portability and small size, is well-suited for crop phenotyping across diverse environments. However, integrating multi-source sensors to achieve synchronized data acquisition and analysis poses significant challenges due to constraints in load capacity and available space. To address these issues, we developed a robotic platform specifically designed for phenotyping greenhouse strawberries. This system integrates an RGB-D camera, a multispectral camera, a thermal camera, and a LiDAR sensor, enabling the unified analysis of data from these sources. The platform accurately extracted key phenotypic parameters, including canopy width (R² = 0.9864, RMSE = 0.0185 m) and average temperature (R²= 0.8056, RMSE = 0.1732 °C), with errors maintained below 5%. Furthermore, it effectively distinguished between different strawberry varieties, achieving an Adjusted Rand Index of 0.94, underscoring the value of detailed phenotyping in variety differentiation. Compared to conventional UGV-LiDAR systems, the proposed platform is more cost-effective, efficient, and scalable, with enhanced data consistency, making it a promising solution for agricultural applications.