# AgriNeRF: Neural Radiance Fields for Agriculture in Challenging Lighting Conditions

arXiv: https://arxiv.org/abs/2409.15487 · HTML: https://mlsanity.com/paper/2409.15487/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2024.

## Built on by (most cited first, 7 of 7)

- [PhysGS: Bayesian-Inferred Gaussian Splatting for Physical Property Estimation](https://mlsanity.com/paper/2511.18570/) (2025)
- [A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond](https://mlsanity.com/paper/2505.00737/) (2025)
- [Multi-Spectral Gaussian Splatting with Neural Color Representation](https://mlsanity.com/paper/2506.03407/) (2025)
- [Diffusion-Denoised Hyperspectral Gaussian Splatting](https://mlsanity.com/paper/2505.21890/) (2025)
- [OmniPlantSeg: Species Agnostic 3D Point Cloud Organ Segmentation for High-Resolution Plant Phenotyping Across Modalities](https://mlsanity.com/paper/2509.21038/) (2025)
- [AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM](https://mlsanity.com/paper/2510.26358/) (2025)
- [Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping](https://mlsanity.com/paper/2511.02207/) (2025)

## Abstract (arXiv)

Neural Radiance Fields (NeRFs) have shown significant promise in 3D scene reconstruction and novel view synthesis. In agricultural settings, NeRFs can serve as digital twins, providing critical information about fruit detection for yield estimation and other important metrics for farmers. However, traditional NeRFs are not robust to challenging lighting conditions, such as low-light, extreme bright light and varying lighting. To address these issues, this work leverages three different sensors: an RGB camera, an event camera and a thermal camera. Our RGB scene reconstruction shows an improvement in PSNR and SSIM by +2.06 dB and +8.3% respectively. Our cross-spectral scene reconstruction enhances downstream fruit detection by +43.0% in mAP50 and +61.1% increase in mAP50-95. The integration of additional sensors leads to a more robust and informative NeRF. We demonstrate that our multi-modal system yields high quality photo-realistic reconstructions under various tree canopy covers and at different times of the day. This work results in the development of a resilient NeRF, capable of performing well in visibly degraded scenarios, as well as a learnt cross-spectral representation, that is used for automated fruit detection.