CROP PRIDICATION USING AI with IOT along with varous data

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1. Project Title/IDEA:  CROP PREDICTION USING AI  Team Member's Name : KUMAR SAURAV UJJWAL KUMAR DUBEY LUCKY RAJ AAYUSHMAN GUPTA ANURAG RAJ Mentor's Name:   Mayanglambam Aarbindro  Singh JIS College of Engineering

2. Challenges in Agriculture : 1. Unpredictable weather impacting crop planning. 2. Pest outbreaks causing significant crop damage . 3. Inefficient resource utilization, leading to higher costs . What is the Solution???? Crop Prediction What is Crop Prediction? Crop prediction is the process of forecasting the type, yield, or health of crops using data-driven approaches. It assists farmers, agricultural experts, and policymakers in making informed decisions . JIS College of Engineering

3. Key Objectives Data Collection : Use IoT sensors to collect real-time data on soil moisture, temperature, humidity, and weather conditions. AI Model Development : Build a machine learning model to predict the best crop based on historical and real-time data. User Interface : Develop a simple mobile or web-based interface for farmers to access predictions. Field Testing : Test the system in real-world conditions with small-scale farmers. Training : Provide training to farmers on how to use the system effectively. JIS College of Engineering

4. Target Audience : - Small and Marginal Farmers Expected Outcomes Improved Crop Yield : Farmers will be able to select the best crop for their soil and weather conditions, leading to higher yields. Cost Savings : Efficient use of resources (water, fertilizers) will reduce costs for farmers. Scalability : The system can be scaled to cover larger areas and more crops in the future. Farmer Empowerment : Small-scale farmers will have access to advanced AI tools, enabling them to compete with larger agricultural enterprises. JIS College of Engineering

5. Idea Description : Core Idea: A cost-effective AI-powered prototype for crop prediction. What It Solves : Helps small-scale farmers decide the best crop using real-time data. The prototype acts as a foundation for scaling up. Challenges Addressed: - Cost Barrier : Using low-cost sensors and open-source software. - Accessibility: SMS-based alerts for farmers without smartphones . JIS College of Engineering

6. Technical Details JIS College of Engineering 1 . IoT Sensors Soil Moisture Sensor : Measures the water content in the soil. Temperature Sensor : Monitors the ambient temperature. Humidity Sensor : Tracks the humidity levels in the air. Weather Station : Collects data on rainfall, wind speed, and other weather parameters. 2. Data Collection Data will be collected in real-time from the sensors and stored in a cloud-based database (e.g., AWS, Google Cloud). Historical data on crops, soil types, and weather patterns will also be used to train the AI model.

7. Technical Details JIS College of Engineering 3 . AI Model Development Data Preprocessing : Clean and normalize the data to remove outliers and missing values. Model Selection : Use machine learning algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks for crop prediction. Model Training : Train the model using historical data and validate it using cross-validation techniques. Real-Time Prediction : The trained model will predict the best crop based on real-time sensor data. 4. User Interface A simple mobile or web-based interface will be developed for farmers to input their location and receive crop predictions. The interface will display the predicted crop, along with recommendations for planting and resource management.

8. Technical Details JIS College of Engineering Data Collection Processing SUPPORT VECTOR MACHIINE (SVM) K-NEAREST NEIGHBORS DESCISION TREE RANDAM FOREST ANN NA/VE BAYES LSTM TRAINING FEATURE EXTRACTION ML/DL MODULES TESTING AND EVALUATION DECISION(BASED ON BEST ML MODEL) NEW CROP DATA PREDICTION AND NEIGHBOUR

9. JIS College of Engineering

10. Budget: JIS College of Engineering Item Cost (INR) Description IoT Sensors ₹30,000 Soil moisture, temperature, humidity, and weather sensors. Software Development ₹20,000 AI model development, mobile/web app development, and database setup. Testing & Field Trials ₹10,000 Field testing in real-world conditions, including travel and logistics. Training for Farmers ₹5,000 Training sessions for farmers on how to use the system. Miscellaneous ₹10,000 Contingency funds for unforeseen expenses. Total ₹75,000  

11. JIS College of Engineering THANK YOU 🙏🏻