Black Pod Rot - Dataset Ninja

Summary #

Black Pod Rot and Pod Borer on Cocoa Pod is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 2436 images with 9377 labeled objects belonging to 6 different classes including cocoa, cocoa_bbox, black_pod_rot, and other: black_pod_rot_bbox, pod_borer, and pod_borer_bbox.

Images in the Black Pod Rot dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (1930 images) and val (506 images). The dataset was released in 2021.

Explore #

Black Pod Rot dataset has 2436 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 Black Pod Rot

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Have a look at 2436 images

Because of dataset's license preview is limited to 12 images

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

Class balance #

There are 6 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 1-6 of 6

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
cocoa_bbox➔ rectangle	2434	3594	1.48	27.08%
cocoa➔ polygon	2434	3595	1.48	20.81%
black_pod_rot_bbox➔ rectangle	623	906	1.45	9.89%
black_pod_rot➔ polygon	623	906	1.45	6.77%
pod_borer_bbox➔ rectangle	105	188	1.79	7.1%
pod_borer➔ polygon	105	188	1.79	4.38%

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.

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 1-6 of 6

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
cocoa polygon	3595	14.07%	42.02%	0.04%	38px	3.52%	2160px	100%	843px	59.08%	19px	1.76%	1074px	72.59%
cocoa_bbox rectangle	3594	18.72%	54.3%	0.06%	37px	3.43%	2160px	100%	843px	59.08%	19px	1.76%	1073px	72.69%
black_pod_rot_bbox rectangle	906	6.89%	41.04%	0.05%	23px	2.13%	999px	92.5%	311px	28.5%	20px	1.85%	621px	57.5%
black_pod_rot polygon	906	4.64%	32.84%	0.03%	23px	2.13%	1000px	92.59%	311px	28.51%	20px	1.85%	621px	57.5%
pod_borer_bbox rectangle	188	3.98%	33.08%	0.07%	29px	2.69%	875px	88.02%	224px	21.28%	19px	1.76%	458px	42.41%
pod_borer polygon	188	2.42%	24.51%	0.04%	30px	2.78%	875px	88.02%	224px	21.31%	19px	1.76%	459px	42.5%

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 9377 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 9377

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➔	cocoa polygon	healthy_2997.jpg	1080 x 1080	992px	91.85%	441px	40.83%	28.75%
2➔	cocoa_bbox rectangle	healthy_2997.jpg	1080 x 1080	992px	91.85%	441px	40.83%	37.51%
3➔	black_pod_rot polygon	healthy_3086.jpg	1080 x 1080	171px	15.83%	88px	8.15%	0.93%
4➔	black_pod_rot_bbox rectangle	healthy_3086.jpg	1080 x 1080	171px	15.83%	88px	8.15%	1.29%
5➔	cocoa polygon	healthy_3086.jpg	1080 x 1080	1001px	92.69%	387px	35.83%	25.06%
6➔	cocoa_bbox rectangle	healthy_3086.jpg	1080 x 1080	1001px	92.69%	387px	35.83%	33.21%
7➔	cocoa polygon	healthy_977.jpg	2160 x 2160	1722px	79.72%	657px	30.42%	18.17%
8➔	cocoa_bbox rectangle	healthy_977.jpg	2160 x 2160	1722px	79.72%	657px	30.42%	24.25%
9➔	cocoa polygon	healthy_977.jpg	2160 x 2160	789px	36.53%	234px	10.83%	2.93%
10➔	cocoa_bbox rectangle	healthy_977.jpg	2160 x 2160	789px	36.53%	234px	10.83%	3.96%

License #

License is unknown for the Black Pod Rot and Pod Borer on Cocoa Pod dataset.

Source

Citation #

If you make use of the Black Pod Rot data, please cite the following reference:

@dataset{Black Pod Rot,
  author={Kenfack Anafack Alex},
  title={Black Pod Rot and Pod Borer on Cocoa Pod},
  year={2021},
  url={https://www.kaggle.com/datasets/kenfackbruno/black-pod-rot-and-pod-borer-on-cocoa-pod}
}

Source

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

@misc{ visualization-tools-for-black-pod-rot-dataset,
  title = { Visualization Tools for Black Pod Rot Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/black-pod-rot } },
  url = { https://datasetninja.com/black-pod-rot },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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Disclaimer #

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