SkyT - Drone Based Crop Management System

Product Introduction

Video Title

Team

Table of Contents

  1. Introduction
  2. Solution Architecture
  3. Hardware & Software Designs
  4. Testing
  5. Detailed budget
  6. Conclusion
  7. Links

Introduction

Tea plantation management faces significant challenges due to inefficient monitoring of vast tea lots, leading to suboptimal crop health and reduced yields. Sky T, a smart drone-based crop management system, addresses these challenges by introducing a real-time monitoring system for tea plantations. This system leverages advanced IoT devices to track soil quality, temperature, and humidity, ensuring optimal growing conditions. Drones play a crucial role by capturing high-resolution images and collecting sensor data from designated nodes, enabling real-time field monitoring. This allows for the early detection of potential issues, improving decision-making and enhancing overall plantation productivity. By integrating smart technologies, Sky T offers a data-driven approach to revolutionizing tea plantation management.

Solution Architecture

Our solution architecture comprises both IoT devices and a cloud-based web application. Sample Image

The high-level overview of the system, as depicted in the accompanying diagram, consists of the following key components.

  1. Sensor Node
  2. Drone
  3. Docking station
  4. Cloud-Based Backend
  5. Web dashboard

1. Sensor Node

The sensor node is a compact environmental monitoring unit installed within a designated tea plantation area, typically covering a perimeter of approximately 500 meters. This unit is designed to collect essential soil and atmospheric data, including pH levels, nutrient content (NPK), temperature, and humidity.

The sensor node operates on either solar power or a rechargeable battery, depending on its location. It remains in a low-power state and is activated only upon receiving a triggering signal from the drone via Bluetooth Low Energy (BLE) communication.

Hardware Components

  1. ESP32 Microcontroller – Handles data acquisition and wireless communication.
  2. RS485 NPK Sensor – Measures nitrogen (N), phosphorus (P), and potassium (K) levels in the soil.
  3. AHT10 Humidity Sensor – Monitors temperature and humidity conditions.

2. Drone

The drone serves as the primary data acquisition unit within the system, collecting environmental data from the sensor node while simultaneously capturing aerial images of the tea plantation. These high-resolution images are processed in the cloud backend to support critical decision-making processes, such as determining the optimal timing for tea harvesting.

To ensure seamless communication with the sensor node and docking station, the drone is equipped with specialized hardware components.

Hardware Components

  1. LoRa Transceiver – Enables long-range, low-power communication between the drone and docking station.
  2. Raspberry Pi Zero Microcontroller – Manages data processing and communication tasks.
  3. Raspberry Pi Camera Module V1.3 – Captures aerial imagery for analysis and decision-making

3. Docking Station

The docking station serves as a critical communication hub within the system, acting as an intermediary between the drone and the cloud platform. Its primary function is to receive data from the drone via a LoRa transceiver and transmit it to the cloud-hosted backend for processing and storage in the database. This ensures seamless data integration and real-time accessibility for analysis and decision-making.

Hardware Components

  1. ESP32 Microcontroller - Manages data transmission and communication processes.
  2. LoRa Transceiver - Facilitates long-range wireless communication between the drone and docking station.
  3. 4G Wifi Dongle - Enables internet connectivity for cloud data transfer.

4. Cloud-Based Backend

The backend infrastructure is hosted on a cloud platform, leveraging Amazon Web Services (AWS) as the cloud service provider. To ensure security and efficiency, the solution is designed with two separate backend systems, one dedicated to processing IoT data and another for managing the web dashboard. Both backends are connected to a centralized database, facilitating seamless data integration while maintaining system security.

Backend Technologies

  1. *IoT Server - Implemented using Python Flask, optimized for handling real-time IoT data processing.
  2. Web Dashboard Server - Developed using TypeScript, ensuring a scalable and maintainable architecture.

Database Management

  1. MongoDB - Used for storing high-resolution aerial images.
  2. MySQL- Handles sensor data storage and user information.

5. Web Dashboard

The web dashboard serves as the user-facing interface, providing real-time insights derived from sensor data analysis. It is designed to support role-based access control, ensuring that users receive dynamic content relevant to their specific roles. This interface enables efficient decision-making by presenting processed data in an intuitive and interactive manner.

Technologies Used

  1. React.js – A modular and scalable framework for building dynamic, responsive user interfaces.
  2. Bootstrap – Enhances the dashboard’s design with a flexible and modern UI framework.

The proposed solution architecture integrates IoT technology, aerial imaging, and cloud computing to create a smart, data-driven crop management system for tea plantations. By leveraging sensor nodes, drones, a docking station, a cloud-based backend, and a web dashboard, the system ensures efficient data collection, processing, and visualization.

Hardware and Software Designs

Software Design

Our software solution is a web-based dashboard built using the following technology stack

  1. Front End - React + Bootstrap
  2. Back End - Typescript
  3. Database - Mongo DB, MySQL
  4. Cloud Service Provider - AWS

Front End Designs

To ensure a seamless user experience that balances simplicity, ease of use, and professionalism, we have developed a fully customizable dashboard with dynamic role-based access control. The system includes secure login and signup validation, adapting the interface dynamically based on the user’s role to enhance usability. Additionally, the design has undergone UI/UX testing, focusing on learnability, usability, and overall user experience, ensuring an intuitive and efficient platform for users.

Testing

Testing done on hardware and software, detailed + summarized results

Detailed budget

All items and costs

Item Quantity Unit Cost Total
NodeMCU 2 2000 LKR 4000 LKR
RS508 Soil Sensor 1 11000 LKR 11000 LKR
DHT 11 Humidity Sensor 1 400 LKR 400 LKR
3300mAh Li-Po Battery 2 3300 LKR 6600 LKR
Drone 1 30000 LKR 30000 LKR
Raspberry Pi Zero 1 3500 LKR 3500 LKR
5MP Raspi Camera Module 1 2500 LKR 2500 LKR
RA02 Lora Module 2 3000 LKR 6000 LKR
32GB Storage Device 2 1790 LKR 3580 LKR
4G Dongle 1 3690 LKR 3690 LKR
Miscellaneous N/A N/A 3000 LKR
SUB TOTAL 14 N/A 74270 LKR

Conclusion

Sky T has successfully reached the proof-of-concept stage, marking a significant milestone before full-scale product development. The complete data path from collecting sensor data and capturing images using a manually flown drone to processing and visualizing the information on a web dashboard has been fully implemented. This achievement validates the system’s feasibility and lays a strong foundation for further enhancements, including automation and scalability. With this progress, Sky T is now poised for the next phase of development, bringing smart, data-driven solutions to tea plantation management.