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darshanjadhav0218@gmail.com
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I'm Darshan Jadhav ! 3rd year BTech student at COEP Technological University in Instrumentation & Control Engineering.Diploma holder in E&TC from Government Polytechnic Nashik.
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Smart Agriculture with AI-powered Crop Monitoring and Disease Prediction
Maixduino Kit for AI+IoT

This project leverages IoT sensors and AI to optimize agricultural practices and improve crop yields. Here's the breakdown:

Deploy IoT Sensors: Install sensors in fields to collect data on environmental factors like temperature, humidity, soil moisture, and light levels.

Image Capture (Optional): Consider integrating cameras or multispectral sensors to capture images of crops at regular intervals.

Data Transmission: Transmit sensor data and potentially image data wirelessly to a central hub or cloud platform.

AI for Crop Monitoring and Disease Prediction: Develop AI models for two key tasks:
Crop Monitoring: Train a model to analyze sensor data and assess crop health based on metrics like growth rate, water stress levels, and nutrient availability.

Disease Prediction: Train another model on historical data (potentially including past crop images with disease identification) to identify early signs of plant diseases based on sensor data and potentially image analysis.

Actionable Insights: The system provides farmers with real-time data visualizations, alerts for potential problems, and recommendations for irrigation, fertilization, and pest control based on AI insights.

Benefits:

Improves crop yields by optimizing resource management (water, fertilizers) based on real-time data.
Enables early detection of plant diseases, allowing for timely intervention and minimizing losses.
Provides data-driven insights to optimize planting schedules and crop selection for specific regions.

Competition Entry Approach:

Focus on a specific crop: Tailor your project to a particular crop type, highlighting the unique challenges and potential benefits of AI-powered monitoring.
Open-source your AI models (optional): Consider contributing your trained AI models to open-source repositories to promote wider adoption in agriculture.
Develop a web app (optional): Design a user-friendly mobile app for farmers to access real-time data visualizations and insights from the system.