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deepNIR Fruit Detection Dataset

4295112394
Tagagriculture
Taskobject detection
Release YearMade in 2022
LicenseCC BY 4.0
Download802 MB

Introduction #

Released 2022-03-15 Β·Inkyu Sa, Jong Yoon Lim, Ho Seok Ahn

The authors of the 4 different DeepNIR datasets are interested in applying data-driven Machine Learning (ML) technologies to the agriculture sector, specifically in core agricultural tasks such as vegetation segmentation and fruit detection. They aim to investigate the potential improvements that can be achieved by adopting ML techniques, specifically synthetic image generation and data-driven object detection. Their initial focus is on generating synthetic near-infrared (NIR) images, which can serve as auxiliary information for object detection.

The NIR spectrum (λ∼750–850 nm) has been of great significance in various agricultural tasks since the 1970s. It has enabled the use of vegetation indices (e.g., NDVI) for assessing vegetation health and conditions. Similar to the thermal spectrum, which provides valuable information beyond the visible range, the NIR spectrum allows for the observation of chlorophyll responses in plants, particularly from leaves. This information is essential for agronomists to understand vegetation status and conditions.

image

To evaluate the impact of synthetically generated images, the authors created a DeepNIR Fruit Detection dataset consisting of 4 channels (NIR+RGB) and 11 fruits, building upon their previous study that presented 7 fruits for detection. The dataset includes blueberry, cherry, kiwi, and wheat, and while the total number of images is relatively small compared to other publicly available datasets, it can still be useful for pre-training models for downstream tasks. Each image contains multiple instances of fruits, as they typically form clusters. Moreover, each fruit image was taken from various camera views, scales, and lighting conditions, facilitating model generalization.

The dataset was split following an 8:1:1 rule for train/validation/test, and the final object detection results were reported using the test set. The authors also addressed errors in their previous dataset, except for the wheat dataset, which was obtained from a machine learning competition. They provide detailed experiment results and dataset samples in the following experiments section of the paper.

Please note, that the authors provided for public download images in .jpg format which doesn’t support the 4th channel (NIR).

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHubDataset LinkZenodoDataset LinkKaggle

Summary #

deepNIR: Dataset for Generating Synthetic near-infrared (NIR) Images and Improved Fruit Detection System Using Deep Learning Techniques is a dataset for object detection and unsupervised learning tasks. It is used in the agricultural industry.

The dataset consists of 4295 images with 161979 labeled objects belonging to 11 different classes including wheat, mango, cherry, and other: kiwi, capsicum, blueberry, apple, rockmelon, orange, avocado, and strawberry.

Images in the deepNIR Fruit Detection dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are 3 splits in the dataset: train (3434 images), test (431 images), and valid (430 images). The dataset was released in 2022 by the CSIRO Data61, Australia and CARES, University of Auckland, Australia.

Here are the visualized examples for the classes:

Explore #

deepNIR Fruit Detection dataset has 4295 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
OpenSample annotation mask from deepNIR Fruit DetectionSample image from deepNIR Fruit Detection
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Have a look at 4295 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 11 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 11
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
wheatβž”
rectangle
3373
147793
43.82
26.83%
mangoβž”
rectangle
170
1145
6.74
14.68%
cherryβž”
rectangle
153
4137
27.04
30.87%
kiwiβž”
rectangle
125
3716
29.73
38.6%
capsicumβž”
rectangle
122
724
5.93
8.28%
blueberryβž”
rectangle
78
3176
40.72
37.68%
appleβž”
rectangle
63
354
5.62
34.54%
rockmelonβž”
rectangle
58
141
2.43
8.33%
orangeβž”
rectangle
57
274
4.81
22.52%
avocadoβž”
rectangle
54
178
3.3
25.19%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 11
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
wheat
rectangle
147793
0.67%
50.66%
0%
1px
0.1%
715px
69.82%
78px
7.61%
1px
0.1%
987px
96.39%
cherry
rectangle
4137
1.38%
18.93%
0.03%
11px
1.22%
885px
58.93%
74px
11.29%
10px
1.28%
838px
45.63%
kiwi
rectangle
3716
1.62%
38.37%
0.01%
8px
1.15%
2156px
100%
74px
12.86%
7px
0.89%
2088px
47.46%
blueberry
rectangle
3176
1.26%
14.47%
0.01%
2px
0.25%
1056px
42.25%
70px
10.22%
7px
1.56%
947px
39.49%
mango
rectangle
1145
2.38%
35.84%
0.25%
18px
4.07%
1116px
74.4%
69px
15.56%
16px
3.8%
843px
63.72%
capsicum
rectangle
724
1.47%
4.64%
0.19%
52px
5.42%
308px
32.08%
139px
14.49%
41px
3.2%
277px
21.64%
apple
rectangle
354
6.79%
29.65%
0.53%
36px
8%
532px
63.43%
169px
27.71%
35px
5.82%
618px
60.35%
strawberry
rectangle
341
3.5%
22.87%
0.27%
28px
6.25%
518px
64.32%
137px
20.86%
23px
3.82%
562px
44.11%
orange
rectangle
274
5%
50.43%
0.29%
21px
5.32%
391px
81.33%
103px
19.92%
26px
5.2%
366px
71.6%
avocado
rectangle
178
8.12%
49.53%
0.39%
28px
7.5%
880px
82.88%
133px
28.44%
23px
5%
629px
66.4%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 161979 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 161979
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
131px
12.79%
112px
10.94%
1.4%
2βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
136px
13.28%
143px
13.96%
1.85%
3βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
83px
8.11%
158px
15.43%
1.25%
4βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
139px
13.57%
160px
15.62%
2.12%
5βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
29px
2.83%
170px
16.6%
0.47%
6βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
95px
9.28%
129px
12.6%
1.17%
7βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
116px
11.33%
126px
12.3%
1.39%
8βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
130px
12.7%
117px
11.43%
1.45%
9βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
118px
11.52%
155px
15.14%
1.74%
10βž”
wheat
rectangle
f15aa967e_jpg.rf.d61b542a815435d11687ec1c59fded69.jpg
1024 x 1024
97px
9.47%
53px
5.18%
0.49%

License #

deepNIR: Dataset for Generating Synthetic near-infrared (NIR) Images and Improved Fruit Detection System Using Deep Learning Techniques is under CC BY 4.0 license.

Source

Citation #

If you make use of the deepNIR data, please cite the following reference:

@dataset{inkyu_sa_2022_6324489,
  author       = {Inkyu Sa and
                  Jong Yoon Lim and
                  Ho Seok Ahn},
  title        = {{deepNIR: Dataset for generating synthetic NIR 
                   images and improved fruit detection system using
                   deep learning techniques}},
  month        = mar,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.1},
  doi          = {10.5281/zenodo.6324489},
  url          = {https://doi.org/10.5281/zenodo.6324489}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-deep-nir-fruit-dataset,
  title = { Visualization Tools for deepNIR Fruit Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/deep-nir-fruit } },
  url = { https://datasetninja.com/deep-nir-fruit },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset deepNIR Fruit Detection can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='deepNIR Fruit Detection', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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