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An IoT-based smart farming solution that provides real-time agricultural data through sensors, a mobile app, and cloud storage.

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Farm-Ware: A Smart Farming Automation System

Farm-Ware is an IoT-driven smart farming solution designed to assist farmers by providing real-time agricultural data such as temperature, soil moisture, humidity, water levels, and plant growth. The project aims to improve farming practices in Bangladesh by making technology accessible to farmers, especially those with limited digital literacy.

Problem Statement

Farming requires a lot of effort, and farmers need to access various pieces of information about their crops and the atmosphere to make informed decisions. However, many farmers in Bangladesh lack knowledge about smart farming and the technology available to make farming more efficient. This project aims to bridge that gap by providing an easy-to-use system that collects real-time data about the farm environment and presents it to farmers via an intuitive mobile app.

Proposed Solution

Farm-Ware collects data through sensors and displays this information in real-time on a mobile application. It is a low-cost, easy-to-use system that allows farmers to access data like temperature, humidity, soil moisture, water flow, and plant growth. The solution aims to save time and effort, offering both immediate and long-term benefits for farmers.

Key Features

  • Low cost for implementation, making it affordable for farmers.
  • Simple design for easy installation and setup.
  • Real-time data access via the mobile app.
  • Machine Learning Integration for predicting plant growth.
  • Cloud storage for easy access to collected data.
  • Location-based search and agricultural advice.
  • Easy-to-use interface that helps farmers monitor crops from anywhere.

Group Members

This project was developed by the following team members from North South University:

  • Tahmim Ali
  • Md. Fahad Hasan Chowdhury
  • Md. Shohaib Islam
  • Jubayer Uddin Shamim

Hardware Components

  • Microcontroller (Arduino/ESP32/ESP8266)
  • Temperature sensor
  • Soil moisture sensor
  • Humidity sensor
  • Water flow sensor
  • Water level sensor
  • Camera for monitoring plant growth

Software Components

  • Mobile App: Developed using Android Studio
  • Backend: MySQL Database for storing user profiles and data
  • Machine Learning: Used to predict the growth rate of plants based on sensor data.
  • Cloud Storage: Firebase for storing sensor data and images.

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An IoT-based smart farming solution that provides real-time agricultural data through sensors, a mobile app, and cloud storage.

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  • Java 52.9%
  • Jupyter Notebook 30.5%
  • Kotlin 11.9%
  • C++ 4.7%