---
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- agritech
- hyperspectral
- spectroscopy
- fruit
- sub-class classification
- detection
size_categories:
- 10K<n<100K
license: mit
---
# Living Optics Hyperspectral Fruit Dataset
## Overview
This dataset contains 100 images of various fruits and vegetables captured under controlled lighting, with the [Living Optics Camera](livingoptics.com).

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

From the 100 images, we extract >430,000 spectral samples, of which >85,000 belong to one of the 19 classes in the dataset. The rest of the spectra can be used for negative sampling when training classifiers.

Additionally, we provide a set of demo videos in `.lo` format which are unannotated but which can be used to qualititively test algorithms built on this dataset.


### Classes

The dataset contains 19 classes:
 - 🍋 lemon - 8275 total spectral samples
 - 🍈 melon - 9507 total spectral samples
 - 🥒 cucumber - 227 total spectral samples
 - 🍏 granny smith apple - 3984 total spectral samples
 - 🍏 jazz apple - 272 total spectral samples
 - 🍎 plastic apple - 6693 total spectral samples
 - 🍎 pink lady apple - 17311 total spectral samples
 - 🍎 royal gala apple - 21319 total spectral samples
 - 🍅 tomato - 3748 total spectral samples
 - 🍅 cherry tomato - 360 total spectral samples
 - 🍅 plastic tomato - 569 total spectral samples
 - 🫑 green pepper - 226 total spectral samples
 - 🫑 yellow pepper - 4752 total spectral samples
 - 🫑 orange pepper - 552 total spectral samples
 - 🍊 orange - 4641 total spectral samples
 - 🍊 easy peeler orange - 2720 total spectral samples
 - 🍐 pear - 194 total samples
 - 🍇 green grape - 106 total spectral samples
 - 🍋‍🟩 lime - 43 total spectral samples


## Requirements

- [lo-sdk](https://www.livingoptics.com/register-for-download-sdk/)
- [lo-data](https://huggingface.co/spaces/LivingOptics/README/discussions/3)


## Download instructions

### Command line
```commandline
mkdir -p hyperspectral-fruit
huggingface-cli download LivingOptics/hyperspectral-fruit --repo-type dataset --local-dir hyperspectral-fruit
```

### Python
```python 
from huggingface_hub import hf_hub_download
dataset_path = hf_hub_download(repo_id="LivingOptics/hyperspectral-fruit", filename="train", repo_type="dataset")
print(dataset_path)
```

## Usage

```python
import os.path as op
import numpy.typing as npt
from typing import List, Dict, Generator
from lo.data.tools import Annotation, LODataItem, LOJSONDataset, draw_annotations
from lo.data.dataset_visualisation import get_object_spectra, plot_labelled_spectra
from lo.sdk.api.acquisition.io.open import open as lo_open

# Load the dataset
path_to_download = op.expanduser("~/Downloads/hyperspectral-fruit")
dataset = LOJSONDataset(path_to_download)

# Get the training data as an iterator 
training_data: List[LODataItem] = dataset.load("train")

# Inspect the data
lo_data_item: LODataItem
for lo_data_item in training_data[:3]:

    draw_annotations(lo_data_item)

    ann: Annotation
    for ann in lo_data_item.annotations:
        print(ann.class_name, ann.category, ann.subcategories)

# Plot the spectra for each class
fig, ax = plt.subplots(1)
object_spectra_dict = {}
class_numbers_to_labels = {0: "background_class"}
for lo_data_item in training_data:
    object_spectra_dict, class_numbers_to_labels = get_object_spectra(
        lo_data_item, object_spectra_dict, class_numbers_to_labels
    )

plot_labelled_spectra(object_spectra_dict, class_numbers_to_labels, ax)
plt.show()    
```

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.