Detecting Corn and Tomato Plant Diseases Using Computer Vision

This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Dar Es Salaam, Tanzania Chapter.

The problem

The traditional methods of disease detection and management rely on visual inspection by farmers can be time-consuming and prone to errors. Early detection of diseases is critical to prevent their spread and minimize crop losses. There is a need for an efficient and accurate system for the detection of crop diseases in maize in Tanzania.

To address this, a machine learning-based system for the detection of crop diseases will be developed, which will provide accurate and reliable diagnoses of crop diseases, enabling farmers to take timely action.

The project team will develop a machine learning-based system to accurately detect crop diseases in maize and tomato crops, using image analysis techniques. The system will be designed to improve crop yields and enhance food security and the livelihoods of farmers.

The machine learning techniques that will be employed in this project are Convolutional Neural Networks (CNNs). CNNs have proven to be highly effective in image recognition and classification tasks, and have been successfully applied in various fields, including agriculture. The trained CNN model will be able to learn and distinguish between different types of crop diseases based on visual symptoms and other parameters. The CNN model will be trained on a dataset of images of healthy and diseased maize and tomato crops, using transfer learning techniques. Transfer learning involves using pre-trained models as a starting point for training, to leverage the learned features and weights from the dataset.

The CNN model will be implemented using the TensorFlow framework, which provides an efficient and flexible platform for building and training machine learning models. The model will be trained using a combination of supervised and unsupervised learning techniques, with the latter being used to identify novel and emerging crop diseases.

Overall, this project will contribute to the development of an innovative solution for the detection and management of crop diseases in Tanzania, with the potential to improve crop yields and livelihoods for farmers.

The goals

The main goal of this project is to develop a machine learning-based system for the detection of crop diseases in maize and tomato crops in Tanzania. The system will use image analysis techniques to identify diseases based on visual symptoms and other parameters. The project will focus on the following goals:

• Develop and train machine learning models to accurately detect and classify crop diseases in maize, using Convolutional Neural Networks (CNNs).
• Developing and training machine learning models to accurately detect and classify crop diseases.
• Building a user-friendly interface for farmers to easily upload images and receive disease diagnosis and management recommendations.
• Evaluating the performance of the system in terms of accuracy, speed, and usability.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work