# Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data

arXiv: https://arxiv.org/abs/2501.10199 · HTML: https://mlsanity.com/paper/2501.10199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study](https://mlsanity.com/paper/2104.00788/) (2021)

## Built on by (most cited first, 1 of 1)

- [In-Field Mapping of Grape Yield and Quality with Illumination-Invariant Deep Learning](https://mlsanity.com/paper/2510.04864/) (2025)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) combined with Hyperspectral imaging (HSI) offer potential for environmental and agricultural applications by capturing detailed spectral information that enables the prediction of invisible features like biochemical leaf properties. However, the data-intensive nature of HSI poses challenges for remote devices, which have limited computational resources and storage. This paper introduces an Online Hyperspectral Simple Linear Iterative Clustering algorithm (OHSLIC) framework for real-time tree phenotype segmentation. OHSLIC reduces inherent noise and computational demands through adaptive incremental clustering and a lightweight neural network, which phenotypes trees using leaf contents such as chlorophyll, carotenoids, and anthocyanins. A hyperspectral dataset is created using a custom simulator that incorporates realistic leaf parameters, and light interactions. Results demonstrate that OHSLIC achieves superior regression accuracy and segmentation performance compared to pixel- or window-based methods while significantly reducing inference time. The method`s adaptive clustering enables dynamic trade-offs between computational efficiency and accuracy, paving the way for scalable edge-device deployment in HSI applications.