  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.
