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Rust and Leaf Miner in Coffee Crop Dataset

54221
Tagagriculture
Taskobject detection
Release YearMade in 2020
LicenseCC BY 4.0
Download896 MB

Introduction #

Released 2020-09-20 ·Lucas Brito Silva, Álvaro Leandro Cavalcante Carneiro, Marisa Silveira Almeida Renaud Faulin

In their pursuit to address the significant challenges posed by pests and diseases in agriculture, the authors of the Rust and Leaf Miner in Coffee Crop dataset have developed an algorithm designed for the detection of rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) within coffee leaves (Coffea arabica). Their solution extends to quantifying disease severity, facilitated through a mobile application functioning as a high-level interface for model inferences.

Within the realm of coffee cultivation, numerous phytosanitary issues abound, including bacteria, fungi, viruses, nematodes, and pests. Among these, rust has emerged as a particularly impactful concern since its inception in East Africa in 1869. Rust inflicts severe harm, leading to the premature defoliation of coffee trees and substantial production declines in subsequent years. Reports indicate production reductions of up to 34%. Similarly, leaf miner infestations, which began in Africa around the mid-1850s, have now become the most prevalent pest globally. Leaf miners damage the leaf epidermis, causing malnourishment and desiccation, resulting in production losses of up to 40%.

To combat these phytopathologies, the authors have turned to artificial intelligence, a field that has witnessed remarkable advancements in recent years. These advancements, particularly in image recognition, owe their success not only to more robust hardware and abundant data but also to the development of new and improved algorithms, including convolutional neural networks (CNNs). CNNs are particularly well-suited for tasks involving image analysis as they can learn and generalize features from images effectively.

Data Acquisition

The dataset for this study was meticulously assembled through the random collection of coffee leaves afflicted with rust and leaf miners from a farm located in Brazil. These leaves were then photographed in a laboratory against a white background using a smartphone camera operating in the RGB color system, which is the standard color space adopted by digital cameras. The RGB color system has demonstrated satisfactory performance in machine-learning applications when compared to other color spaces.

The resulting dataset comprises 285 samples of rust-infested leaves and 257 samples of leaves affected by the leaf miner. Initially, the images were captured at a resolution of 4000 x 2250 pixels. However, to align with the standards set by Liu et al. for Single Shot MultiBox Detector (SSD) and to optimize training time and computational resources, the images were resized to dimensions of 300 x 300 pixels and 512 x 512 pixels.

Creating a labeled dataset poses one of the central challenges in object detection techniques. It necessitates the manual annotation of object locations within images, often involving the delineation of bounding boxes. This process is not only resource-intensive but also time-consuming and costly, particularly when numerous objects per image need to be labeled, specialized knowledge is required, or the dataset is extensive.

Data Labeling

The authors describe that the initially collected photos underwent manual labeling using LabelImage software. However, to streamline this labor-intensive process and expedite future dataset expansion, an algorithm was devised for automatic image labeling, adopting a semi-supervised approach. Consequently, for each new unlabeled image, a pre-trained model, fueled by a limited number of strongly-labeled images, inferred the objects of interest.

The automatically generated labels underwent a meticulous review process due to inherent inaccuracies in detecting objects within certain images. Unaddressed inaccuracies could potentially diminish model accuracy. Nevertheless, this revised labeling process, albeit more rapid and cost-effective, continues to demand less human effort compared to conventional approaches.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkKaggle

Summary #

Rust (Hemileia Vastatrix) and Leaf Miner (Leucoptera Coffeella) in Coffee Crop (Coffea Arabica) is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 542 images with 3487 labeled objects belonging to 2 different classes including rust and leaf miner.

Images in the Rust and Leaf Miner in Coffee Crop 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 2020 by the São Paulo State Faculty of Technology, Brazil.

Dataset Poster

Explore #

Rust and Leaf Miner in Coffee Crop dataset has 542 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 Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
OpenSample annotation mask from Rust and Leaf Miner in Coffee CropSample image from Rust and Leaf Miner in Coffee Crop
👀
Have a look at 542 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
Images
Objects
Count on image
average
Area on image
average
rust
rectangle
285
2242
7.87
2.42%
leaf miner
rectangle
257
1245
4.84
14.47%

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 1-2 of 2
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
rust
rectangle
2242
0.31%
8.62%
0.02%
37px
1.06%
1317px
32.92%
144px
6.31%
36px
0.9%
976px
26.18%
leaf miner
rectangle
1245
3.04%
23.53%
0.08%
74px
3.29%
1410px
62.67%
436px
19.36%
80px
2%
2273px
56.83%

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 3487 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 3487
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
rust
rectangle
name319.jpg
2250 x 4000
107px
4.76%
242px
6.05%
0.29%
2
rust
rectangle
name319.jpg
2250 x 4000
293px
13.02%
157px
3.92%
0.51%
3
rust
rectangle
name319.jpg
2250 x 4000
159px
7.07%
126px
3.15%
0.22%
4
rust
rectangle
name319.jpg
2250 x 4000
112px
4.98%
131px
3.27%
0.16%
5
rust
rectangle
name319.jpg
2250 x 4000
104px
4.62%
101px
2.52%
0.12%
6
rust
rectangle
name319.jpg
2250 x 4000
156px
6.93%
159px
3.98%
0.28%
7
rust
rectangle
name319.jpg
2250 x 4000
123px
5.47%
112px
2.8%
0.15%
8
rust
rectangle
name319.jpg
2250 x 4000
74px
3.29%
107px
2.67%
0.09%
9
rust
rectangle
name319.jpg
2250 x 4000
131px
5.82%
163px
4.08%
0.24%
10
rust
rectangle
name319.jpg
2250 x 4000
102px
4.53%
148px
3.7%
0.17%

License #

Rust (Hemileia Vastatrix) and Leaf Miner (Leucoptera Coffeella) in Coffee Crop (Coffea Arabica) is under CC BY 4.0 license.

Source

Citation #

If you make use of the Rust and Leaf Miner in Coffee Crop data, please cite the following reference:

Brito Silva, Lucas; Cavalcante Carneiro, Álvaro Leandro; Silveira Almeida Renaud Faulin, Marisa (2020), 
“rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee crop (Coffea arabica).”, 
Mendeley Data, V5, 
doi: 10.17632/vfxf4trtcg.5

Source

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

@misc{ visualization-tools-for-rust-and-leaf-miner-in-coffee-crop-dataset,
  title = { Visualization Tools for Rust and Leaf Miner in Coffee Crop Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rust-and-leaf-miner-in-coffee-crop } },
  url = { https://datasetninja.com/rust-and-leaf-miner-in-coffee-crop },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Rust and Leaf Miner in Coffee Crop 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='Rust and Leaf Miner in Coffee Crop', 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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