---
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- agritech
- hyperspectral
- spectroscopy
- fruit
- sub-class classification
- detection
size_categories:
- 10K<n<100K
license: other
---

# Living Optics Orchard Dataset

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66aa0ad8f46d069c6339c72c/oVtV8fWMlXljp9BKQEaGq.png)

## Overview
This dataset contains 435 images of captured in one of the UK's largest orchards, using the Living Optics Camera.

The data consists of RGB images, sparse spectral samples and instance segmentation masks.

The dataset is derived from 44 unique raw files corresponding to 435 frames. 
Therefore, multiple frames could originate from the same raw file. 
This structure emphasized the need for a split strategy that avoided data leakage. 
To ensure robust evaluation, the dataset was divided using an 8:2 split, with splitting performed at the raw file level rather than the frame level. 
This strategy guaranteed that all frames associated with a specific raw file were confined to either the training set or the test set, eliminating the 
risk of overlapping information between the two sets.
The dataset contains 3,785 instances of Royal Gala Apples, 2,523 instances of Pears, and 73 instances of Cox Apples, summing to a total of 6,381 labelled instances. 

The spectra which do not lie within a labelled segmentation mask can be used for negative sampling when training classifiers.


Additional unlabelled data is available upon request.


## Classes
The training dataset contains 3 classes:
- 🍎 cox apple - 3,605 total spectral samples
- 🍎 royal gala apple - 13,282 total spectral samples
- 🍐 pear - 34,398 total spectral samples

The remaining 1,855,755 spectra are unlabelled and can be considered a single "background " class.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66aa0ad8f46d069c6339c72c/st56aYWI5Lj1-2oqQzerj.png)

> This is a notebook showing how hyperspectral data, collected with the Living Optics camera.

## Requirements

- [lo-sdk](https://cloud.livingoptics.com/)
- [datareader](https://github.com/livingoptics/datareader.git)


## Download instructions

You can access this dataset via the [Living Optics Cloud Portal](https://cloud.livingoptics.com/shared-resources?file=data%2Fannotated-datasets%2Fhyperspectral-orchard.zip).

See our [Spatial Spectral ML](https://github.com/livingoptics/spatial-spectral-ml) project for an example of how to train and run a segmentation and spectral classification algoirthm using this dataset.

## Usage

```python
import os
import numpy as np
import matplotlib.pyplot as plt
from lo_dataset_reader import DatasetReader, spectral_coordinate_indices_in_mask, rle_to_mask

os.environ["QT_QPA_PLATFORM"] = "xcb"

dataset_path = "/path/to/dataset"
dataset = DatasetReader(dataset_path, display_fig=True)

for idx, ((info, scene, spectra, unit, images_extern), (converted_spectra, converted_unit), annotations, library_spectra, labels) in enumerate(dataset):
    for ann_idx, annotation in enumerate(annotations):
        annotation["labels"] = labels

        # Visualise the annotation on the scene
        dataset.save_annotation_visualisation(scene, annotation, images_extern, ann_idx)

        # Get spectrum stats from annotation
        stats = annotation.get("extern", {}).get("stats", {})
        label = stats.get("category")
        mean_radiance_spectrum = stats.get("mean_radiance_spectrum")
        mean_reflectance_spectrum = stats.get("mean_reflectance_spectrum")

        # Get mask and spectral indices
        mask = rle_to_mask(annotation["segmentation"], scene.shape)
        spectral_indices = spectral_coordinate_indices_in_mask(mask, info.sampling_coordinates)

        # Extract spectra and converted spectra
        spec = spectra[spectral_indices, :]
        if converted_spectra is not None:
            conv_spec = converted_spectra[spectral_indices, :]
        else:
            conv_spec = None

        # X-axis based on band index or wavelengths (optional)
        x = np.arange(spec.shape[1])
        if stats.get("wavelength_min") is not None and stats.get("wavelength_max") is not None:
            x = np.linspace(stats["wavelength_min"], stats["wavelength_max"], spec.shape[1])

        # Determine plot layout
        if converted_spectra is not None:
            fig, axs = plt.subplots(2, 2, figsize=(12, 8))
            axs_top = axs[0]
            axs_bottom = axs[1]
        else:
            fig, axs_top = plt.subplots(1, 2, figsize=(12, 4))
            print(f"Warning: No converted_spectra for annotation '{label}'")

        unit_label = unit.capitalize() if unit else "Radiance"

        # (1,1) Individual spectra
        for s in spec:
            axs_top[0].plot(x, s, alpha=0.3)
        axs_top[0].set_title(f"{unit_label.capitalize()} Spectra")
        axs_top[0].set_xlabel("Wavelength")
        axs_top[0].set_ylabel(f"{unit_label.capitalize()}")

        # (1,2) Mean + Min/Max (Before conversion)
        if mean_radiance_spectrum is not None:
            spec_min = np.min(spec, axis=0)
            spec_max = np.max(spec, axis=0)
            axs_top[1].fill_between(x, spec_min, spec_max, color='lightblue', alpha=0.5, label='Min-Max Range')
            axs_top[1].plot(x, mean_radiance_spectrum, color='blue', label=f'Mean {unit_label.capitalize()}')
            axs_top[1].set_title(f"Extern Mean ± Range ({unit_label.capitalize()})")
            axs_top[1].set_xlabel("Wavelength")
            axs_top[1].set_ylabel(f"{unit_label.capitalize()}")
            axs_top[1].legend()

        # (2,1) and (2,2) Only if converted_spectra is available
        if converted_spectra is not None and conv_spec is not None:
            for s in conv_spec:
                axs_bottom[0].plot(x, s, alpha=0.3)
            axs_bottom[0].set_title(f"{converted_unit} Spectra")
            axs_bottom[0].set_xlabel("Wavelength")
            axs_bottom[0].set_ylabel(f"{converted_unit}")

            if mean_reflectance_spectrum is not None:
                conv_min = np.min(conv_spec, axis=0)
                conv_max = np.max(conv_spec, axis=0)
                axs_bottom[1].fill_between(x, conv_min, conv_max, color='lightgreen', alpha=0.5, label='Min-Max Range')
                axs_bottom[1].plot(x, mean_reflectance_spectrum, color='green', label=f'Mean {converted_unit}')
                axs_bottom[1].set_title(f"Extern Mean ± Range ({converted_unit})")
                axs_bottom[1].set_xlabel("Wavelength")
                axs_bottom[1].set_ylabel(f"{converted_unit}")
                axs_bottom[1].legend()

        fig.suptitle(f"Annotation {label}", fontsize=16)
        plt.tight_layout()
        plt.show()
```

![image/png](https://huggingface.co/datasets/LivingOptics/hyperspectral-orchard/resolve/main/media/hyperspectral-orchard.png)

## Citation

Raw data is available by request