Garden Monitoring System Using AI

GARDEN MONITORING SYSTEM USING AI

Our project introduces a Garden Monitoring Robotic AI System that combines cutting-edge AI and robotics to transform garden and agricultural management. This innovative system is designed to monitor plant health, identify pests, and optimize fertilization processes. By integrating advanced sensors and robotic technology, it provides a holistic solution for efficient and eco-friendly gardening.

PROJECT MOTIVATION

The traditional methods of managing gardens and farms often involve labor-intensive and inefficient practices. We created this system to streamline these tasks, reducing manual effort and enhancing overall efficiency. Our aim is to tackle common gardening and farming challenges, making the process more manageable and promoting sustainable agricultural practices.

WORKING

The system utilizes the AMD Kria™ KR260 Robotics Starter Kit to interface with various sensors and components. Data from sensors measuring soil moisture, temperature, humidity, and light is gathered by an Arduino. This information is then processed by the AMD processor, which uses Python-based deep learning algorithms to analyze camera feeds for signs of plant diseases. The system provides recommendations for fertilization through the MIT App and a Telegram bot. Additionally, it integrates with ThingSpeak for real-time data monitoring and automates irrigation based on soil moisture readings. Future plans include adding a robotic arm for automated camera adjustments and watering, incorporating advanced sensors, and enhancing scalability for larger agricultural setups.

RESULTS

1. Continuous Camera Monitoring and Analysis:

The system continuously streams live video from a camera connected to the AMD Kria™ KR260 Robotics Starter Kit. This footage is processed using advanced deep learning algorithms implemented in Python to identify and diagnose plant diseases. The processed video feed, including images of diseased leaves, is transmitted in real-time to both the MIT App and a Telegram bot.

2. Comprehensive Data Integration and User Alerts:

The MIT App consolidates and displays real-time data from various sensors, such as soil moisture, temperature, humidity, and light levels. It also provides notifications about plant health issues, including visual evidence of diseases from the camera feed. This integration allows users to monitor and respond to plant conditions efficiently.

3. Telegram Bot for Disease Alerts and Fertilization Suggestions:

The Telegram bot complements the MIT App by delivering detailed updates directly to users. It provides information about detected plant diseases, including images of affected leaves, and offers tailored fertilizer recommendations based on the analysis. This dual-channel approach ensures that users receive prompt and actionable insights for managing their gardens effectively.

CONCLUSION

The Garden Monitoring Robotic AI System marks a notable progression in automating and refining garden and agricultural management. By leveraging the AMD Kria™ KR260 Robotics Starter Kit along with cutting-edge sensors and advanced deep learning algorithms, the system proficiently oversees plant health, identifies diseases, and delivers customized fertilization recommendations. Real-time analysis of camera feeds and sensor data ensures that users receive timely and precise information, which is relayed through both the MIT App and a Telegram bot, enabling quick responses and informed decision-making.

This project effectively tackles critical challenges in garden and agricultural management, such as efficient monitoring, pest control, and optimized resource utilization. Looking ahead, planned enhancements, including a robotic arm and advanced sensors, will further elevate the system’s performance and scalability, making it suitable for larger-scale agricultural applications. In essence, this system not only boosts operational efficiency but also supports sustainable practices, setting the stage for smarter, more effective garden management.