Cassava-leaf-disease-classification

Computer Vision - Identify the type of disease present on a Cassava Leaf image.

As the second-largest provider of carbohydrates in Africa, cassava is a key food security crop grown by smallholder farmers because it can withstand harsh conditions. At least 80% of household farms in Sub-Saharan Africa grow this starchy root, but viral diseases are major sources of poor yields.

And the existing methods of disease detection require farmers to solicit the help of government-funded agricultural experts to visually inspect and diagnose the plants. This suffers from being labor-intensive, low-supply and costly. As an added challenge, effective solutions for farmers must perform well under significant constraints, since African farmers may only have access to mobile-quality cameras with low-bandwidth.

In this project, a dataset of 21,367 labeled images are used which is collected during a regular survey in Uganda by Makerere University. Most images were crowdsourced from farmers taking photos of their gardens, and annotated by experts at the National Crops Resources Research Institute (NaCRRI). This is in a format that most realistically represents what farmers would need to diagnose in real life.

The goal of the project is to classify each cassava image into four disease categories or a fifth category indicating a healthy leaf. A better model will help farmers to quickly identify diseased plants,and potentially save their crops before they inflict irreparable damage.

Dataset

The dataset can be downloaded from here .

Get involved

Start by installing Python and Git if you have not already.

Next, clone this project by opening a terminal and typing the following commands (do not type the first $ signs on each line, they just indicate that these are terminal commands):

$ git clone https://github.com/bprasad26/cassava-leaf-disease-classification.git 
$ cd cassava-leaf-disease-classification

Next, create a virtual environment:

# create virtual environment
$ python3 -m venv leaf_venv
# Activate the virtual environment
$ source leaf_venv/bin/activate

Install the required packages

# install packages from requirements.txt file
$ pip install -r requirements.txt

Create folder to store data

$ mkdir data

Download the data from the link given above. Extract it and put it in this folder.

deactivate the virtual environment, once done with your work

$ deactivate

to stay updated with this project

$ git pull

Files

explore.py - exploratory data analysis
helper.py - helper functions for various tasks
model_dispatcher - models that will be used for training
train.py - script for training the model