Disease Detection in Fruit Images

Introduction #

Released 2021-09-01 ·Chang Hee Han, Eal Kim, Tan Nhu Nhat Doanet al.

The authors of the Disease Detection in Fruit Images dataset utilized 74 images containing apple fruits to assess the effectiveness of their proposed neural network. These images featured single or multiple apples, with at least one of them being afflicted by anthracnose. The disease manifests as rounded symptoms with varying sizes and visible pattern variations. A skilled expert marked the regions of these rounded symptoms on apple fruits using bounding boxes, providing the ground-truth labels for the study. A total of 182 bounding boxes were created, ranging from 1 to 9 per image. The apple images varied in size from 500x700 to 3000x2000 pixels, while disease symptom sizes ranged from 50x50 to 2000x1400 pixels.

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Research Paper

Summary #

Dataset: Region Aggregated Attention CNN for Disease Detection in Fruit Images is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 74 images with 182 labeled objects belonging to 1 single class (anthracnose).

Images in the Disease Detection in Fruit Images dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021 by the Sejong University, Korea.

Explore #

Disease Detection in Fruit Images dataset has 74 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.

Sample annotation mask from Disease Detection in Fruit Images

Sample image from Disease Detection in Fruit Images

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Have a look at 74 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
anthracnose➔ rectangle	74	182	2.46	12.58%

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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.

Rows 1-1 of 1

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
anthracnose rectangle	182	5.2%	40.16%	0.13%	67px	3.84%	1589px	69.73%	480px	23.8%	55px	1.95%	1789px	58.24%

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.

Objects #

Table contains all 182 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.

Rows 1-10 of 182

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	anthracnose rectangle	IMG_3304.JPG	1704 x 2272	1171px	68.72%	1035px	45.55%	31.31%
2➔	anthracnose rectangle	IMG_0920.JPG	2112 x 2816	127px	6.01%	123px	4.37%	0.26%
3➔	anthracnose rectangle	DSCN4610.JPG	2304 x 3072	633px	27.47%	673px	21.91%	6.02%
4➔	anthracnose rectangle	DSCN4610.JPG	2304 x 3072	909px	39.45%	565px	18.39%	7.26%
5➔	anthracnose rectangle	DSCN5211.JPG	768 x 1024	263px	34.24%	249px	24.32%	8.33%
6➔	anthracnose rectangle	IMG_6427.JPG	2736 x 3648	845px	30.88%	801px	21.96%	6.78%
7➔	anthracnose rectangle	DSC_0240.JPG	1488 x 2240	179px	12.03%	171px	7.63%	0.92%
8➔	anthracnose rectangle	DSC_0240.JPG	1488 x 2240	207px	13.91%	163px	7.28%	1.01%
9➔	anthracnose rectangle	DSC_0240.JPG	1488 x 2240	93px	6.25%	75px	3.35%	0.21%
10➔	anthracnose rectangle	DSC_0240.JPG	1488 x 2240	157px	10.55%	171px	7.63%	0.81%

License #

Dataset: Region Aggregated Attention CNN for Disease Detection in Fruit Images is under CC BY 4.0 license.

Source

Citation #

If you make use of the Disease Detection in Fruit Images data, please cite the following reference:

@dataset{Disease Detection in Fruit Images,
	author={chhan95},
	title={Dataset: Region Aggregated Attention CNN for Disease Detection in Fruit Images},
	year={2021},
	url={https://github.com/QuIIL/Dataset-Region-Aggregated-Attention-CNN-for-Disease-Detection-in-Fruit-Images}
}

Source

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

@misc{ visualization-tools-for-disease-detection-in-fruit-images-dataset,
  title = { Visualization Tools for Disease Detection in Fruit Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/disease-detection-in-fruit-images } },
  url = { https://datasetninja.com/disease-detection-in-fruit-images },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset Disease Detection in Fruit Images 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='Disease Detection in Fruit Images', 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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