Drought Prediction Using ML and Weather Data for Agriculture
Objective
The objective of this project is to develop an accurate drought prediction system using machine learning techniques. It utilizes advanced algorithms such as Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM) to classify drought conditions with high precision based on meteorological data, including rainfall, humidity, temperature, wind speed, and soil moisture. The system is integrated with a Flask-based web application that allows users to register, log in, evaluate model performance, and make real-time drought predictions. The goal is to create a reliable, scalable, and user-friendly platform for efficient drought monitoring and management.
Abstract
This project
focuses on developing a machine learning-based drought prediction system using
meteorological data to support agricultural studies. Drought is a condition
characterized by an extended period of insufficient rainfall, resulting in
reduced soil moisture and limited crop growth. Predicting drought accurately is
a challenging task due to the dynamic and nonlinear nature of weather
parameters such as temperature, rainfall, humidity, wind speed, and soil
moisture. The objective of this work is to analyze these meteorological factors
and forecast drought conditions using data-driven approaches.
Keywords: Drought Prediction, Random Forest, XGBoost, LightGBM, Machine Learning, Weather Data, Agriculture, Flask, Model Evaluation, Climate Analysis
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W CONFIGURATION:
Processor - I3/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
RAM - 8GB
S/W CONFIGURATION:
• Operating System : Windows 7/8/10
• Server side Script : HTML, CSS, Bootstrap & JS
• Programming Language : Python
• Libraries : Django, Pandas, MySQL.Connector, Scikit-Learn
• IDE/Workbench : VS Code
• Technology : Python 3.8+
• Server Deployment : Xampp Server
Demo Video
Request for Video
Paper PublishingRequest Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
