🎓 This challenge is a part of the Application of Machine Learning in Plant Sciences Summer School.
Plant diseases and pests affect the normal growth of plants and even cause plant death during the growth process. Crop diseases can become a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. But with easy access to phones and cameras, these diseases can be captured and identified to avoid plant death and improve crop yield.
Can you detect plant diseases just by examining the images of the plant’s leaves, even when the resolution is low? Participants must correctly identify a disease class from low-resolution images of plant leaves in this task.
Get started with this easy-2-follow notebook.
The dataset consists of
43525 images of plants leave at
32 pixels. Each of the images belongs to one of the
38 disease classes (some of these classes also represent healthy leaves). A separate test dataset of
10779 images is provided, and the task is to predict the associated disease (from the
38 valid disease classes) for each of the images. The training and the test set reflect numerous real-world properties of datasets, such as class imbalance (some classes have about
~4500 images, while some classes can have as low as
~120 images in the training set) etc.
The images for both the training and the test set are aggregated into individual
Numpy array files for convenient programmatic access.
The following files are available in the
43466samples) A numpy file with an
(43525, 32, 32, 3)representing all the images in the training set.
43466samples) A numpy file with a
(43525,)representing the corresponding labels for the images in the training set. This array consists of a unique index per class. And mapping of the index to a human-readable class name can be found in
10838samples) A numpy file with an
(10779, 32, 32, 3)representing all the images in the test set.
all_classes.txt- A file containing the mapping of the class index used in the
train-labels.npyfile, and the
sample_submission.csvfile to that of a human-readable class name. The file contains 38 lines, where the line index of the class name in the file is the index of the class names in all the label files.
sample_submission.csv- A sample submission file to provide a reference about the expected file format for the submission system to work.
- Prepare a CSV containing a header as
class_indexand the predicted class index
[0-38)with the name as
- Sample submission format available as
sample_submission.csvin the resources section.
- Plant diseases and pests detection based on deep learning
- Using Deep Learning for Image-Based Plant Disease Detection
- Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
- Plant leaf disease detection using computer vision and machine learning algorithms
🖊 Evaluation Criteria
NOTE: Only participants of the Application of Machine Learning in Plant Sciences Summer School 2022 are eligible for the prizes.
[Getting Started] ETH PSC Summer School Hackathon