Abstract
Fasal Mitra is an open-source AI & Machine Learning project. An automated plant disease detection, Progressive Web App that will help the farmers to detect the disease in their crops and will also give insights on its treatment . This folder contains most of the backend. The backend follows a microservice architecture, there are many microservices nd these services communicate with each other through HTTP requests and the fronend communicates with the frontend via an API-gateway. It is built using JavaScript, React, Docker, Express, Node.js. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
This folder contains most of the backend. The backend follows a microservice architecture, there are many microservices nd these services communicate with each other through HTTP requests and the fronend communicates with the frontend via an API-gateway. The authentication service uses mongoDB and redis cache for user authentication. THe dl service uses ResNet9 as Deep Learning Model and flask server for deploying this model into an API, while the nginx acts as an API gateway.
2. Objective
An automated plant disease detection, Progressive Web App that will help the farmers to detect the disease in their crops and will also give insights on its treatment .
This project demonstrates how JavaScript, React, Docker can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Node.js (LTS) and npm
- A modern web browser
- VS Code or any code editor
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/FASAL-MITRA-SIH-22/Fasal-Mitra.git
cd Fasal-MitraFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by FASAL-MITRA-SIH-22 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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