PlantGenius

Repository Plant Genius team Capstone C23-PS187

The Team Members

We are C23-PS187 product-based capstone project team, consisting of 6 members in three learning paths as follows:

• Machine Learning Path:
  1. M286DSX0042 - Yogi Saputra - Universitas Negeri Semarang
  2. M357DSX1774 - Wildan Assyidiq - Universitas Teknologi Digital Indonesia
• Cloud Computing Path:
  1. C357DSX3196 - Ilham Muliawan - Universitas Teknologi Digital Indonesia
  2. C286DSY0804 - Mitha Anggrainy - Universitas Negeri Semarang
• Mobile Development Path:
  1. A286DSX1231 - Dio Puja Andika - Universitas Negeri Semarang
  2. A316DSX1123 - Retno Adi Saputra - Universitas Perwira Purbalingga

The Project Overview

Commodity plant diseases affect crop yield and quality, causing significant economic losses in agriculture. Traditional methods for detecting commodity plant diseases rely on visual inspection, which can be time-consuming and require expertise. Recent advancements in computer vision and deep learning techniques have shown potential in providing an accurate and efficient solution to commodity plant disease detection. The proposed project aims to leverage these advancements and develop an automated system for plant disease detection that can be easily deployed in the real world.

The Project Scope

Each of learning path in this team works collaboratively to deliver the product within the scope below:

• Develop PlantGenius as a mobile app to provide information and education about various kinds of Plant Disease Commodities and how to deal with them such as, the story behind the disease and how to overcome it and this application is intended to improve plant growth rates and overcome diseases so that export commodity crops increase both domestically and abroad.
• The machine learning team will create machine learning models to classify commodity plant diseases by leveraging the TensorFlow library to build Neural Network (Deep Learning) designs and using datasets from Kaggle.
• The Cloud Computing team will set up a cloud service to deploy machine learning model in compute engine and use cloud SQL and cloud storage to store data such as plant disease and user data. We also made a rest api application as a data communication.
• The Mobile Development team will create a UI Design for the application and implement it to the android application, then completing the application with API endpoint from Cloud which contains the commodity plant disease dataset and ML model that has been trained.

Disclaimer

• PlantGenius may be developed later for beneficial use for stakeholders in a collaborative manner.
• PlantGenius aims to engage in business areas, such as providing paid drug and fertilizer provider e-commerce features as well as local weather prediction services under future development.