The project consists of hardware circuit which has an ESP8266 microcontroller connected with DHT11 and Capacitive Soil Moisture sensors. Both of these sensors arerelatively inexpensive and provide fairly accurate values. The ESP8266 microcontroller also known as Node MCU is useful for enabling WiFi connectivity. Users can easily connect to the Node MCU via their mobile phone. Similarly they can connect their computer to the phone WiFi and hence data exchange and communication will be facilitated. As the data recorded from the sensors is stored in locally hosted database there is no additional cost to the user. The third party analytics service that is used in this project i.e. ThingSpeak limits the free tier version of its service to 3 million messages per year and message update interval time to 15 seconds. The feature of downloading sensor data into a CSV format requires MS Excel or any other application that can read file stored in this format. The system also features a Machine Learning based crop recommendation system that is integrated into the main website. Upon giving the required environmental and soil parameters, the program recommends most suitable crop that could be grown by running a highly accurate Machine Learning Model. This helps the farmer to choose crops that may give maximum yield. As India is a huge country impacted by the vagaries of climate, appropriate crop selection becomes a question of utmost importance.
A. Node MCU connectivity
Users have to first connect their mobile phone to the Node MCU. This is required to establish stable connection. Users should also have their device connected to their computer.
B. Data acquisition from sensors
The values returned by the soil moisture sensor and DHT11 sensor in real time are sent to a locally hosted Apache server and are stored in MySQL database. The same data is also sent to third party analytics service ThingSpeak, in which a channel has been created to record and display real time change in sensor values by plotting a value vs timestamp graph.
C. Login page
A login page has been created that needs user name and password for authentication. In order to login to the system to view the main webpage users need to provide their email ID as username and a 6 letter password. Authentication has been achieved by writing a code snippet in JavaScript.
D. Sensor Data
The real time values stored in the database are being constantly updated and are displayed on an automatically updating web page. The web page shows data such as date and time, location and sensor values. This interface has been developed with HTML and CSS and PHP has been used in the backend to fetch data from the database. The web interface links to all the features provided by the system.
E. Motor Status
If the user notices that the soil moisture level readings taken in real time are somehow less than what is predetermined than the user can click on motor status link which will take the user to a new page where he/she will be provided with a button through which an on command can be sent to the microcontroller. The microcontroller which receives the command can transmit it to a relay. The relay sends the signal to motor which is finally turned on. After sometime when moisture level has been restored the user can press the same button on the motor status webpage that could send an off signal to the microcontroller. The microcontroller passes this signal to the motor through relay and the motor is finally turned off.
F. Data Collection Dashboard
The data collection dashboard shows a webpage that displays the frequently updating values from the ThingSpeak channel. Users have not been granted direct access to ThingSpeak channel due to privacy and security concerns related to API keys.
G. Export as CSV
If the user wants to download a snapshot of the sensor readings taken during any particular time, he/she can do so by going to the export as CSV page here the sensor readings along with the timestamp (in GMT) have been made available. Uses can download the CSV file and open it by using an appropriate application like MS Excel or Google Sheets. This is useful if the user wants to run his/her own analytics locally such as generating descriptive statistics on the sensor data. This feature has been developed using Express.js and makes use of EJS for generating HTML markup.
H. Crop Recommendation System
A crop recommendation system has been developed which predicts the most suitable crop based on a variety of environmental and soil parameters. 22 different types of crops are recommended by this system. The dataset used for training and testing of the machine learning model has been acquired from https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset. The parameters used in this system are Nitrogen, Phosphorus and Potassium content and pH value of the soil, Temperature, Humidity and average Rainfall in mm. Some of the data is collected i.e. Temperature and Humidity has been collected from the sensors earlier. NPK content and pH of the soil can be obtained from district wise government soil testing laboratory and average rainfall of every district can be obtained from Indian Meteorological Department (IMD) website. After satisfying the input constraints of the application, the program outputs the recommended crop after running a Machine Learning model. The model used in this application is K Nearest Neighbours, which gives a highly accurate result. This feature has been developed mainly using Python library Scikit-learn for machine learning tasks and PySimpleGUIWeb for user interface.