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Cocoa Diseases Dataset

31231
Tagagriculture
Taskobject detection
Release YearMade in 2020
LicenseODbL v1.0
Download2 GB

Introduction #

Serrano Arenas Juan Sebastián, Torres Villamizar Camilo Andrés

The authors of Cocoa Diseases (YOLOv4): Monilia & Phytophthora (Diseases in Cocoa Pods) claim that the total production of Theobroma cacao L cocoa in Colombia for 2018 exceeded 56 thousand tons, making it the second-highest in history despite a 6% reduction compared to 2017. The decrease in production was attributed to factors such as flowering flows, increased incidence of the Monilia disease, and floods caused by heavy rainfall early in the year. In response to the need for reliable inspection procedures to assess crop infections, the authors of the dataset developed a mobile application prototype that utilizes artificial intelligence techniques and image analysis to identify diseased cocoa pods.

The research collected data to create a dataset containing data on the most concerning diseases affecting cocoa crops, such as Phytophthora and Monilia. The authors employed YOLOv4, a machine learning algorithm, to train a model with a 60% accuracy in detecting cocoa pods. The results show promise for the application’s potential usefulness as a mobile tool for farmers and agricultural researchers. The tool can aid in decision-making processes, providing an accurate evaluation of cocoa pod infections without the need for an expert trained in cocoa crop phytosanitary management.

To consolidate and standardize the database for calibration, training, and development activities, the authors conducted a data collection survey that involved capturing step images of the cocoa pods. They collaborated with SENA (National Service of Learning) and visited the agricultural sector’s attention center in Playón, Santander. The images were captured following standardized procedures, including setting specific camera characteristics such as focal point, exposure time, ISO speed, lens compensation exposure, focal length, maximum aperture, metering mode, and flash mode. The images were then labeled and coded using the LabelImg tool to distinguish between diseased and healthy cocoa pods. The set of images containing diseased and healthy cocoa pods was consolidated for later use in training the machine learning model.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Cocoa Diseases (YOLOv4): Monilia & Phytophthora (Diseases in Cocoa Pods) is a dataset for an object detection task. It is used in the agricultural industry, and in the agricultural research.

The dataset consists of 312 images with 1591 labeled objects belonging to 3 different classes including healthy, phytophthora, and monilia.

Images in the Cocoa Diseases dataset have bounding box annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: all (312 images). The dataset was released in 2020 by the Autonomous University of Bucaramanga, Colombia.

Dataset Poster

Explore #

Cocoa Diseases dataset has 312 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 Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
OpenSample annotation mask from Cocoa DiseasesSample image from Cocoa Diseases
👀
Have a look at 312 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 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 1-3 of 3
Class
Images
Objects
Count on image
average
Area on image
average
healthy
rectangle
257
1219
4.74
9.9%
phytophthora
rectangle
164
227
1.38
9.38%
monilia
rectangle
123
145
1.18
11.92%

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.

Search
Rows 1-3 of 3
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
healthy
rectangle
1219
2.18%
25.77%
0.01%
34px
0.82%
3141px
75.5%
599px
14.41%
20px
0.64%
1529px
49.01%
phytophthora
rectangle
227
6.84%
46.76%
0.04%
100px
2.4%
3510px
84.38%
1153px
27.73%
46px
1.47%
2064px
66.15%
monilia
rectangle
145
10.11%
53.52%
0.11%
178px
4.28%
3720px
89.42%
1427px
34.62%
56px
1.79%
2464px
78.97%

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 1591 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 1591
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
monilia
rectangle
Monilia98.jpg
4160 x 3120
2809px
67.52%
1042px
33.4%
22.55%
2
healthy
rectangle
Monilia98.jpg
4160 x 3120
1847px
44.4%
701px
22.47%
9.98%
3
healthy
rectangle
Monilia98.jpg
4160 x 3120
546px
13.12%
647px
20.74%
2.72%
4
monilia
rectangle
Monilia27.jpg
3120 x 4160
348px
11.15%
895px
21.51%
2.4%
5
phytophthora
rectangle
Monilia27.jpg
3120 x 4160
615px
19.71%
934px
22.45%
4.43%
6
healthy
rectangle
Sana73.jpg
4160 x 3120
683px
16.42%
309px
9.9%
1.63%
7
healthy
rectangle
Sana73.jpg
4160 x 3120
719px
17.28%
374px
11.99%
2.07%
8
healthy
rectangle
Sana73.jpg
4160 x 3120
534px
12.84%
174px
5.58%
0.72%
9
healthy
rectangle
Sana73.jpg
4160 x 3120
728px
17.5%
311px
9.97%
1.74%
10
healthy
rectangle
Sana73.jpg
4160 x 3120
446px
10.72%
242px
7.76%
0.83%

License #

Cocoa Diseases (YOLOv4): Monilia & Phytophthora (Diseases in Cocoa Pods) is under ODbL v1.0 license.

Source

Citation #

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

@misc{20.500.12749_13367,
      author = {Serrano Arenas Juan Sebastián and Torres Villamizar Camilo Andrés},
      title = {Prototipo de aplicación móvil para la identificación de mazorcas de cacao enfermas haciendo uso de visión por computadora y aprendizaje de máquina},
      year = {2020},
      abstract = {Según fuentes de Fedecacao, para el 2018 se determinó que la producción total de cacao Theobroma cacao L en Colombia superó las 56 mil toneladas, la segunda más alta en toda la historia puesto que hubo una reducción de la producción del 6% en comparación al 2017, al pasar de 60 mil a 56 mil toneladas (Fedecacao, 2018). La reducción se dio debido a los flujos de floración, al incremento de la enfermedad llamada Monilia y a las inundaciones causadas por las precipitaciones de comienzos de año. Teniendo en cuenta que la inspección semanal es un procedimiento confiable para evaluar el grado de infección en los cultivos, el presente proyecto desarrolla un prototipo de aplicación móvil que permita identificar mediante técnicas de inteligencia artificial y análisis de imágenes las mazorcas de cacao enfermas. Mediante el levantamiento de datos esta investigación realiza la consolidación de un conjunto de datos que contiene las enfermedades más preocupantes como son la Fitóftora y la Monilia. Para mejorar el procedimiento de inspección se entrena una máquina de aprendizaje con YOLOv4 obteniendo un 60% de precisión en la detección de mazorcas de cacao. Los resultados prometen una herramienta móvil útil a los agricultores e investigadores agrícolas para la toma de decisiones, permitiendo evaluar y realizar con precisión el proceso de inspección de las mazorcas sin la necesidad de un experto capacitado en el manejo fitosanitario en los cultivos de cacao.},
      url = {http://hdl.handle.net/20.500.12749/13367}
      }

Source

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

@misc{ visualization-tools-for-cocoa-diseases-dataset,
  title = { Visualization Tools for Cocoa Diseases Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cocoa-diseases } },
  url = { https://datasetninja.com/cocoa-diseases },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Cocoa Diseases 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='Cocoa Diseases', 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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