Smart Plant Watering Tracker

Gardening

The client had an existing gardening system but needed a way to make the sensors communicate with their platform. Our job was to ensure data moved smoothly between the plant sensors, the central gateway, and the backend system. The central sensor collected information from all the plant sensors and sent it to the server. This data could then be viewed through the mobile app we developed.

Development Process

The development process focused on identifying our specific needs, determining the required features, the communication range between sensors, understanding how existing solutions work, identifying their shortcomings, and incorporating those insights into our project. As a result, we defined the key requirements, and all subsequent actions were directed toward achieving them. The development ultimately provided the following functionalities:

Custom Plant Profiles: Users can add their plants to the interface or select from pre-loaded profiles. They input necessary growth parameters, which the system uses to monitor and predict growth.
Photo and Data Logging: As the plant grows, users can periodically upload photos and record measurements such as height. This data is fed into the system to refine growth predictions and provide tailored advice.
AI-Powered Insights and Alerts: The program analyzes all collected data and compares it with optimal conditions for the specific plant species. It then generates real-time alerts if any parameters fall outside the recommended range, prompting user intervention.
Historical Data Analysis: The application maintains a comprehensive history of all environmental conditions and plant responses. Over time, the AI engine learns from this data, improving the accuracy of its recommendations and predictions.

Core Technologies Employed

To achieve this, we leveraged a robust tech stack that includes:

Ionic Framework & Angular: These frameworks were utilized to develop a cross-platform application that works seamlessly on Android, iOS, and as a Progressive Web App (PWA). This ensures a consistent user experience across all devices.
NestJS & TypeScript: The backend was developed using NestJS and TypeScript, providing a scalable, maintainable, and type-safe environment. This setup handles data processing, user management, and API communication.
REST API Interface: For tasks that do not require real-time interaction, such as triggering actions or querying data, we implemented a REST API. This interface ensures smooth communication between the frontend and backend systems.
Security Measures: Data security is a top priority. We employed client SSL certificates for authorization, ensuring secure communication between the devices and the server.
AWS Lambda: To manage the backend processes efficiently, we used AWS Lambda, a serverless compute service. This pay-per-use model is cost-effective and scales automatically with the application’s needs, minimizing operational costs.
Database and Storage: The solution uses an Amazon RDS database for storing application data. We opted for a scalable solution, with both vertical and horizontal scaling options, to handle increasing amounts of data. Additionally, S3 storage was used for static files, log storage, and backup purposes.
Route 53: For DNS management, we used AWS Route 53, ensuring reliable routing of traffic within the AWS infrastructure.

Key Features and Functionalities

Real-Time Data Logging:

As the plant grows, users can periodically upload photos and record data such as the plant’s height. The program analyzes this data, offering insights and recommendations to improve plant health and growth.

Remote Monitoring and Control:

Users can monitor and manage their plants remotely. This feature provides continuous access to growth data and environmental conditions, enabling users to make adjustments and stay informed no matter where they are.

Custom Plant Profiles:

Users can either select from pre-existing plant profiles or create custom profiles for their plants. The program allows users to input specific growth parameters, which are then used to monitor and predict plant growth.

AI-Driven Insights:

The application uses AI, powered by TensorFlow, to analyze environmental data and provide recommendations. These insights evolve over time, improving as more data is collected.

Critical Alerts and Notifications:

The program is designed to detect when the growing conditions deviate from optimal levels. It sends real-time alerts to users, ensuring timely intervention to prevent potential issues.

Comprehensive History and Forecasting:

The application maintains a detailed history of the plant’s growth conditions, using this data to predict future growth and even link to weather forecasts to plan necessary actions.

Conclusion: A Testament to Our Technical Expertise

The system we developed shows how we can bring together advanced technology into an easy-to-use solution. With AI, strong security, and cloud infrastructure, we created a tool that helps users take better care of their plants. This project highlights our ability to deliver innovative solutions that meet the needs of our clients, showing our strength in the R&D field.