| Project Overview | This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction 'Data Optimisation of Machine Learning Models for Smart Irrigation in Urban Parks'. The project connects agricultural engineering with artificial intelligence, machine learning, deep learning, IoT, computer vision, drone analytics, or RAG-style decision support. Students can use the linked 2023-onward paper/source as the academic base and convert it into an implementation-focused final-year project with sensors, datasets, dashboards, mobile/web interfaces, prediction models, or prototype automation. |
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| Research Paper Title | Data Optimisation of Machine Learning Models for Smart Irrigation in Urban Parks |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2024 |
| Project Area | Water Management Projects |
| Project Type | Sensor Network Optimization |
| Required Tools / Software | Arduino/ESP32, soil moisture sensor, DHT sensor, relay module, Python/Flask, Firebase/MySQL, ML model |
| Main Features / Working Principle | Use clustering and sensor data optimization to reduce water monitoring cost |
| Expected Output | A water sensor network optimization dashboard |
| Possible Add-ons | Add missing sensor prediction |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "Data Optimisation of Machine Learning Models for Smart Irrigation in Urban Parks" in "Agricultural Engineering" |
This B.Tech agricultural engineering project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.