A web portal for farmers integrated with ML and IoT to analyze daily crops and their growth. The main purpose of initiating this undertaking for my "Final Year Project" was to get an insight on the integration of hardware, software and AI (learning).
Project Report available at KrishiSathi Docs
I would like to describe each building process separately and reflect my experiences and tools that I used throughout the development phase.
I first started the project focusing on the hardware part (me being a Robotics enthusiast definitely helped with works related to embedded systems)
- The device was build around Arduino as the brain (or microcontroller) along with sensors like pH Meter (SKU: SEN0161), humidity & temperature sensor (DHT22) and soil moisture sensor (SEN-13322)
- The values obtained from the sensors acted as the feature for our machine learning model
- To collect the measured data from soil (with the help of sensors), I build an android application using the MIT App Inventor 2 because I did not have proper knowledge of JAVA and Android Studio
| Android Application | Hardware Schematics |
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The web application was developed in Django. It was particularly important to create an interface for a user (farmer in our case) to have an insight of over their crops and soil condition.
- The user would simply sign up using the web application and receive a user id
- Next thing is to connect the IoT device to the android app via bluetooth (we used HC-06 bluetooth module in the device for that purpose)
- Once the android app connects to the device and starts receiving real time data, the user can submit it to the web application's database.
- The submitted data gets through the trained model and different graphs and insights on the real data are shown on the web app; along with the classification of suitable crops that can be grown on the field
| Web Application |
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We used a classical machine learning technique involving decision trees--called Random Forest Classifier
- The trained model yielded 84.6% accuracy on the testing set
- The dataset was accessed and refined from the Ministry of Agriculture, Nepal with the help of our supervisor and his contacts in the soil department
- The prominent features chosen for the machine learning model were pH, soil_moisture, temperature, and humidity
- We trained the model with around 48,000 data points classifying eight major types of soils--each corresponding to their favorable crops and fruits
- The classified soil type and its corresponding suitable crops and fruits are displayed as recommendation for the user (or farmer) in the web portal
The instructions below will get you a copy of the project up and running on your local machine for development and testing purposes. To get started, clone the repository on a folder that you want.
The major things that you will need to run this application are Python3 and pip3
$ sudo apt-get install python3
$ sudo apt-get install python3-pip
After you get the python3 and the pip3 installed and working, you will need to install the requirements to run your project. The django application resides inside the folder named "krishisathi"
For that purpose, go to the root directory of the repository and run the following command:
$ pip3 install -r requirements.txt
Note: You might want to use a virtual environment for installing the python packages required for the project
$ sudo pip3 install virtualenv
$ virtualenv krishisathi
$ source path/to/env/bin/activate
For database services, you can either use the provided Sqlite3 database from Django or else you can setup your own MySQL or Postgres database service. The configurations for the project is decoupled in env.example file. You need to copy that file to a separate file named .env inside krishisathi/ folder. You local configurations are the ones that go inside the .env file
Note: If you are using the pre-provided sqlite3 from Django, then please tweak the database settings in the settings.py file. You can use the default configurations instead of the one that I've used.
- Django - The web framework used
- MIT App Inventor 2 - Android app building tool
- scikit-learn - The Machine Learning library used
- Ashish Jaiswal - Initial work - Project Lead - Ashish Jaiswal
- Manish Adhikari - Project Member - Manish Adhikari
- To the personnel who were involved in contributing to the project from Kathmandu Engineering College, TU