ZHAGARAM - SMART SERICULTURE SYSTEM

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  software

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  hardware

Inspiration

The traditional sericulture industry faces challenges such as pests, environmental fluctuations, and disease outbreaks that impact productivity and sustainability. Our goal was to harness AI, IoT, and automation technologies to provide real-time solutions for farmers, helping them make informed decisions and improve silk yield while minimizing manual intervention.

What it does

ZHAGARAM is a Smart Sericulture System designed to revolutionize the sericulture industry by:

• Detecting pests and diseases in real-time using YOLOv8-powered object detection models.
• Monitoring environmental conditions like temperature, humidity, and light through IoT sensors to maintain optimal conditions.
• Automating water sprinkling systems based on environmental data.
• Providing a user-friendly app integrated with Firebase to visualize data, monitor the environment remotely, and receive alerts.
• Offering a chatbot for instant assistance in pest control and environment management.

How we built it

Hardware: ESP32 microcontrollers, IoT sensors (for temperature, humidity, etc.), and automatic sprinklers. Software:

• AI & ML: YOLOv8 for pest and disease detection.
• App Development: Firebase for backend data storage, React Native for the user interface.
• Cloud: Firebase for real-time data management.
• Automation: Actuators to control sprinklers based on environmental thresholds.
• Chatbot: Integrated chatbot to assist farmers in managing pest and environmental issues. ## Challenges we ran into -Data collection and model accuracy: Collecting relevant images to train the pest detection model took time, and achieving high accuracy with YOLOv8 was challenging. -IoT sensor calibration: Ensuring accurate environmental readings required several iterations and fine-tuning. -Real-time connectivity: Integrating IoT devices and Firebase without data loss or delays was a complex task. -User experience: Designing an intuitive interface for farmers with minimal technical expertise required multiple usability tests. ## Accomplishments that we're proud of -Successfully implementing YOLOv8 for real-time pest detection with a high level of accuracy. Building a seamless IoT system that automates environmental control and helps maintain optimal farming conditions. -Developing a responsive and user-friendly app that simplifies monitoring and management for sericulture farmers. -Creating a chatbot that adds value by providing instant pest control advice. -Enhancing productivity and sustainability in sericulture farming, contributing to the agricultural community. ## What we learned -AI for agriculture: Gained experience in implementing object detection models like YOLOv8 for practical farming use cases. -IoT integration with automation: Learned how to collect, transmit, and visualize sensor data in real-time for decision-making. -Cross-functional collaboration: Coordinating hardware, software, and cloud components to deliver an end-to-end solution. -User-centric design: Improved skills in developing solutions that are accessible and intuitive for non-technical users.

What's next for ZHAGARAM - SMART SERICULTURE SYSTEM

-Expand the pest detection model to cover more species and diseases. -Incorporate predictive analytics for early warnings on environmental changes and pest outbreaks. -Integrate advanced AI tools like LLMs (Large Language Models) to improve chatbot capabilities. -Introduce multilingual support to make the app more accessible to farmers across different regions. -Pilot testing with farmers to refine the system based on real-world feedback and expand its usage to other agricultural domains.

Built With

• iot
• javascript
• llm
• mistralai
• python
• reactnative