Abstract
The Smart Drought Detection System using Satellite Data and Real-Time AI Weather Analysis integrates IoT hardware with intelligent data processing to provide early drought prediction and monitoring. The hardware unit is built around an ESP8266 microcontroller, which collects real-time environmental data from sensors such as DHT11 (temperature and humidity sensor), soil moisture sensor, and rain sensor. The system also features an LCD display to show local real-time readings of temperature, humidity, soil moisture, and rainfall status. The collected data is transmitted to the ThingSpeak cloud platform for visualization and analysis. In areas with limited internet connectivity, a GSM module ensures timely alerts via SMS.
By combining ground sensor data with satellite-based weather indicators and processing it through machine learning algorithms, the system can accurately detect and predict drought conditions. This hardware-driven IoT solution, with the addition of a local LCD display for immediate readings, provides a cost-effective, scalable, and efficient system for proactive water resource management and agricultural planning.
Introduction
Drought is a severe environmental challenge that affects agriculture, water resources, and the livelihoods of millions worldwide. Early detection and monitoring are crucial to minimize its adverse effects on crops, livestock, and communities. Traditional methods rely on manual observation or historical data, which are often slow, inaccurate, and unable to provide real-time insights.
The Smart Drought Detection System using Satellite Data and Real-Time AI Weather Analysis addresses these limitations by combining IoT hardware, cloud computing, and machine learning for accurate and timely drought monitoring. The system employs an ESP8266 microcontroller to collect environmental data from sensors such as DHT11 (temperature and humidity sensor), soil moisture sensor, and rain sensor. An LCD display provides real-time local readings of these parameters, allowing immediate monitoring at the site. The data is transmitted to the ThingSpeak cloud platform for storage, visualization, and further analysis. A GSM module ensures reliable alerts via SMS in areas with limited internet connectivity.
By integrating ground sensor data with satellite-based weather indicators and processing it through machine learning algorithms, the system can detect and predict drought conditions efficiently. The combination of IoT hardware, LCD visualization, cloud analytics, and AI provides a cost-effective, real-time, and scalable solution for proactive agricultural management and water resource planning.




Problem Statement
Drought is a significant environmental issue that leads to water scarcity, reduced agricultural productivity, and economic losses, particularly in regions dependent on farming. Traditional drought monitoring methods rely on manual observation and historical data, which are often slow, inaccurate, and unable to provide real-time information. Farmers and authorities frequently face delays in receiving critical data, limiting their ability to take timely preventive measures.
There is a pressing need for a real-time, automated, and cost-effective system that can monitor environmental conditions continuously, predict drought trends accurately, and provide instant feedback to users. By integrating IoT sensors (DHT11 for temperature and humidity, soil moisture sensor, and rain sensor) with a local LCD display, farmers can immediately see current field conditions. Additionally, cloud platforms like ThingSpeak and GSM-based alerts ensure that data reaches decision-makers even in remote areas. Combining these hardware components with machine learning algorithms and satellite data allows for a comprehensive, intelligent, and proactive drought detection system.
Objectives
The main objective of the Smart Drought Detection System using Satellite Data and Real-Time AI Weather Analysis is to develop a real-time, automated, and intelligent system for early drought detection. The specific objectives are:
1. To monitor environmental parameters such as temperature, humidity, soil moisture, and rainfall using IoT sensors (DHT11, soil moisture sensor, and rain sensor).
2. To display real-time sensor readings locally on an LCD display for immediate monitoring.
3. To transmit real-time data to a cloud platform (ThingSpeak) via ESP8266.
4. To send alerts via GSM module in areas with limited internet connectivity.
5. To integrate satellite weather data with ground sensor data for comprehensive drought analysis.
6. To analyze collected data using machine learning algorithms for accurate prediction of drought severity and trends.
7. To provide timely alerts and notifications to farmers and authorities for proactive water resource management and crop protection.
8. To develop a cost-effective and scalable system that can be implemented in agricultural regions to reduce the impact of drought.