Aarogya Sheti

Abstract

1. Introduction

(Back to top) Plant disease can directly lead to stunted growth causing bad effects on yields. An economic loss of up to $20 billion per year is estimated all over the world. Diverse conditions are the most difficult challenge for researchers due to the geographic differences that may hinder the accurate identification. In addition, traditional methods mainly rely on specialists, experience, and manuals, but the majority of them are expensive, time-consuming, and labor-intensive with difficulty detecting precisely. Therefore, a rapid and accurate approach to identify plant diseases seems so urgent for the benefit of business and ecology to agriculture. In agriculture products, diseases are the main cause for the lessening in both quality and production of the agriculture products. Farmers puts their great effort in picking best seeds of plant and also provide proper environment for the growth of the plant, although there are lot of diseases that affects plant result in plant disease. Recognition of the deleterious regions of plants can be considered as the solution for saving the reduction of crops and productivity. The past traditional approach for disease detection and classification requires enormous amount of time, extreme amount of work and continues farm monitoring. Our project is regarding Plant health monitoring system and its disease detection using Machine Learning. Our project basically consists of two parts : 1) Plant health monitoring system :- These IOT device will give real time temperature, humidity and moisture value to check good conditions for crops including fertility of soil. 2) Plant disease detection :- We have developed an Android application that detects plant diseases using Convolutional Neural Network (CNN) using Plant Village dataset will contains 54,304 images of 14 different crops species. So, our main aim is to detect the plant disease in the early stage which can help us to minimize the damage, reduce production costs, and rise the income.

A Smart-Farming solution for farmers to ease the process of farming with the help of IOT and ML . It provides the farmers a way to monitor their farms with IOT smart solutions and early plants disease detection through ML.

2. Objective

A Smart-Farming solution for farmers to ease the process of farming with the help of IOT and ML . It provides the farmers a way to monitor their farms with IOT smart solutions and early plants disease detection through ML.

This project demonstrates how Machine Learning, Flutter 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.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/anotherwebguy/AarogyaSheti.git
cd AarogyaSheti

Full 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.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by anotherwebguy and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.