Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields

TL;DR

This paper demonstrates that Neural Radiance Fields can accurately perform in-situ 3D plant phenotyping in greenhouses, offering a scalable and robust alternative to traditional 3D scanning methods.

Contribution

It introduces a novel application of NeRF for plant phenotyping in greenhouse environments, showing comparable accuracy to existing 3D scanning techniques.

Findings

NeRF achieves a mean distance error of 0.865mm.

NeRF provides similar accuracy to traditional 3D scanning methods.

NeRF offers improved scalability and robustness for in-situ phenotyping.

Abstract

Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyping of plants in farm environments. This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments. To quantitatively evaluate the performance of this method, traditional point cloud registration on 3D scanning data is implemented for comparison. Experimental result shows that NeRF(Neural Radiance Fields) achieves competitive accuracy compared to the 3D scanning methods. The mean distance error between the scanner-based method and the NeRF-based method is 0.865mm. This study shows that the learning-based NeRF method achieves similar accuracy to 3D scanning-based methods but with improved scalability and robustness.