Creating a Rice Disease Classifier using Open Source Data and Computer Vision

Local Chapter Philippines Chapter

Coordinated byPhilippines ,

Status: Completed

Project Duration: 27 Mar 2023 - 22 Apr 2023

Open Source resources available from this project

Project background.

Rice is the heart of Filipino cuisine, an ingredient so fundamental to the country’s food culture that it is eaten at every meal, from breakfast to dinner. But beyond being a staple food, rice has become deeply ingrained in the fabric of Philippine society, serving as a symbol of the country’s identity and values.

It’s served at every meal with almost every dish, including meats, seafood, vegetables, and soups. Rice also plays a significant role in Filipino celebrations and rituals, and is a symbol of community and shared identity. Its rich cultural significance speaks to the deep connection Filipinos have with their land, their food, and each other.

The problem.

Rice diseases are a major concern in the Philippines, a country that relies heavily on rice as a staple food. A variety of fungal, bacterial, and viral diseases can infect rice plants, causing reduced yield, lower quality, and even total crop loss. These diseases can be devastating to farmers, especially those with limited resources who cannot afford the cost of chemical treatments or disease-resistant seeds. Additionally, the high humidity and frequent rain in the Philippines create favorable conditions for the growth and spread of rice diseases.

Project goals.

We aim to deploy a deep learning model through a website or a mobile application that will identify different rice diseases by simply uploading a photo of a rice crop.With a duration of four weeks, this project aims to: - Research about Different Rice Diseases in the Philippines - Data Collection and Data Cleaning - Model Building - Model Deployment

Project plan.

  • Week 1

    Research about Different Rice Diseases in the Philippines

  • Week 2

    Data Collection and Data Cleaning

  • Week 3

    Model Building

  • Week 4

    Model Deployment

Learning outcomes.

– Crop image processing
– Computer vision
– Project management
– Model deployment
– Data preprocessing

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