Precision Farming Robot

Project Overview

The Precision Farming Robot project explores the use of automation for weed management in farming. As an early prototype, it demonstrates the basics of weed detection and targeting. The project aims to reduce labor and herbicide usage, while laying the groundwork for more advanced agricultural robotics. The robot uses a camera to capture images of the surrounding environment, and basic image processing techniques to identify weeds. A laser pointer is then used to target the detected weeds, simulating weed elimination. The robot's movement is controlled via a desktop application, and data is exchanged through the MQTT protocol. This project highlights the potential of automation in agriculture, and future developments will focus on integrating AI for better weed detection, improving navigation, and adding more advanced sensors.

Precision Farming Robot

Automated Weed Detection and Targeting

Key Features

• Automated Weed Detection: Uses basic image processing techniques to detect weeds in a controlled environment.
• Targeting Mechanism: A laser pointer targets detected weeds for simulation of weed elimination.
• Controlled Navigation: The robot's movement is controlled via a desktop application within a limited environment.
• Modular Design: Easy to update and extend with new features, such as AI or better navigation.
• Decentralized Control: Independent control of each module, simplifying the design and development process.

Technologies

Benefits

• Weed Detection: Introduces automated weed detection as a proof of concept for more efficient farming methods.
• Labor Reduction: Aims to reduce manual labor for tasks like weed monitoring and elimination.
• Herbicide Reduction: By targeting weeds directly, the robot can reduce the need for herbicides, promoting sustainable farming practices.

Precision Farming Robot Application

Rover Controller Application

How It Works

The robot uses a camera to capture images of the surrounding environment. Basic image processing identifies weeds, and the robot moves to target them. The laser pointer is controlled by a servo motor for precise targeting. The robot is controlled via a desktop application, and data is exchanged through the MQTT protocol. This early prototype focuses on demonstrating the potential of automation in weed management and will be developed further in future versions.

Precision Farming Robot Architecture

Automated Weed Detection and Targeting

Challenges & Future Plans

• AI Integration: Future development will incorporate machine learning algorithms to enhance weed detection accuracy. The goal is to enable the robot to distinguish between weeds and crops in complex environments and automatically adjust its targeting strategy based on factors such as plant size and growth stage.
• Improved Navigation: Future versions will integrate GPS and advanced path-planning algorithms, allowing the robot to autonomously navigate large agricultural fields. Obstacle detection and avoidance systems will be implemented to ensure efficient operation in challenging terrains, providing a robust solution for field coverage.
• Scalability: The modular design will be further developed to handle larger systems for industrial-scale farms. The aim is to build a fleet of smaller robots that can work together autonomously, providing an efficient solution for larger farms while reducing the need for human supervision.
• Advanced Sensors: Future iterations will include additional sensors, such as thermal cameras or hyperspectral imaging, to enhance weed detection under various lighting conditions and improve precision in identifying specific types of weeds or other potential crop threats.
• Data Analytics: The robot will be integrated with cloud-based systems to collect and analyze data on weed density, soil conditions, and crop health. This data can be used to generate reports and insights for farmers, enabling better decision-making and fostering data-driven farming practices.
• Sustainability: Renewable energy sources, such as solar panels, will be incorporated to make the robot more energy-efficient and sustainable, minimizing the carbon footprint of its operations.
• Collaboration with Farmers: Future work will involve close collaboration with farmers to fine-tune the robot’s design and functionality. Gathering feedback from real-world users will be crucial to ensure the robot is adaptable and can meet the unique challenges of different types of farms.

This first prototype is just the beginning, and the development of new features is underway. With future improvements, the robot has the potential to revolutionize weed management in agriculture, making farming more sustainable and efficient.

IoT Plant Monitoring System

Project Overview

I'm excited to share my IoT Plant Monitoring System—a project born from my passion for technology and plant care. Using an ESP32 microcontroller, this system tracks key health indicators like humidity, temperature, and soil moisture, and sends the data to the cloud via AWS IoT Core. It's all about using tech to make plant care smarter and easier!

Key Features

• Real-Time Monitoring: Stay updated on your plant's health around the clock.
• Cloud Integration: Access your data from anywhere, anytime, with AWS IoT Core.
• Accurate Time Sync: Precise data logging with NTP server synchronization.
• Precise Data Collection: Using DHT11 and soil moisture sensors for accurate readings.

Technologies

Benefits

• Smart Plant Care: Real-time insights for keeping your plants healthy and happy.
• Remote Access: Monitor your plants wherever you are.
• Reliable Records: Accurate, time-stamped data for peace of mind.

How It Works

• Data Collection: Sensors gather real-time data on plant conditions.
• Data Transmission: Sends data to AWS IoT Core using MQTT for remote monitoring.
• User Access: Check plant conditions from any device, anywhere you are.

IoT Plant Monitoring System

It Sends Data to AWS IoT Core using MQTT protocol.

Challenges & Opportunities

• Client Application: Building a user-friendly app to make monitoring even easier. Your input and ideas are highly appreciated!

Join Me!

• Custom PCB: Looking for help to design an integrated PCB.
• More Sensors: Let's explore adding more sensors for deeper insights.
• 3D Printed Case: Interested in creating a portable, sleek 3D-printed case? Let's collaborate!

If you’re passionate about IoT or just love plants, I’d love to have you join me on this journey. Let’s build something awesome together!

Battery Degradation Trajectory Prediction

Introduction

A dynamic project centered around the in-depth analysis of time-series data, specifically focusing on battery performance. Leveraging Python's powerful libraries, including pandas andmatplotlib, this project aimed to unravel critical insights fromCSV files, shedding light on the intricate behavior of various battery parameters over time.

My Role

1. Conceptualized the data analysis approach.
2. Implemented data loading, refinement, andmanipulation strategies.
3. Developed customplotting functions and threshold-based analysis.
4. Conducted capacity and voltage charging analyses, applying domain specific knowledge.
5. Oversaw datamerging and filtering processes for comprehensive insights.

Technologies

Battery Degradation Trajectory Prediction

Data-Driven Lifespan: Python Predictions for Battery Health

Open Inventory

Introduction

Using the Qt framework and C++, Open Inventory is an open-source inventory management system. The system offers a complete solution for tracking and managing assets, to streamline and improve inventory management for enterprises.

My Role

1. Custom menus for administrators, vendors, and customers provide an intuitive interface.
2. Order Placement Made Easy: Enables users to place orders and generate invoices with ease.
3. Effective Inventory Management: Provides a dedicated interface for suppliers, making it easier to add stock.
4. Strong Control: User Administration: Effective Instruments for Managing Vendor and Customer Profiles.
5. Financial analytics: Sturdy instruments that offer information on profit and loss to help with decision-making.

Technologies

Open Inventry

Open sourse inventry system.

Hostel Management System

Introduction

The Hostel Management System is a comprehensive solution for managing hostel operations. It offers features for hostel booking, maintenance requests, user administration, and more. Developed in Java with an MVC architecture, it aims to streamline hostel management for administrators, students, and staff.

My Role

1. Custom menus for administrators, maintenance staff, and students to ensure smooth system operation.
2. Streamlined order placement for students to book hostels easily.
3. Developed a user management system for handling administrator, staff, and student profiles effectively.

Technologies

Hostel Management System

Efficient management of hostel bookings and operations.