🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

Can you detect plant diseases just by examining the images of the plant’s leaves? Sounds Tough. Can you do the same task if the images are at a really low resolution? Welcome to your new challenge!

In this task participants have to correctly `identify` a `disease-class` from low-resolution `images` of `plant-leaves`

Understand with code! Here is some code to get you started right away! 😄

## 💾 Dataset

The dataset consists of `43525` images of plant leaves at `32`x`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.

## 📁 Files

Following files are available in the `resources` section:

• `train-images.npy` - (`43466` samples) A numpy file with an `ndarray` of shape `(43525, 32, 32, 3)` representing all the images in the training set.
• `train-labels.npy` - (`43466` samples) A numpy file with a `ndarray` of shape `(43525,)` representing the corresponding labels for the images in the training set. This array consists of a unique index per-class. And a mapping of the index to a human-readable class name can be found in `all_classes.txt`.
• `test-images.npy` - (`10838` samples) A numpy file with an `ndarray` of shape `(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.npy` file, and the `sample_submission.csv` file 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 labels files.
• `sample_submission.csv` - A sample submission file to provide a reference about the expected file format for the submission system to work.

## 🚀 Submission

• Prepare a CSV containing header as `class_index` and the predicted class index `[0-38)` with the name as `submission.csv`.
• Sample submission format available as `sample_submission.csv` in the resources section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score and Accuracy Score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$