# Conclusion

This project successfully developed an AI-based system for detecting and counting rabbits in a farm environment using advanced object detection techniques. It was a multifaceted endeavor, requiring meticulous planning, experimentation, and iterative problem-solving to achieve the desired results.

**Summary of Work**

The project spanned several phases:

* **Data Preparation:** Extracting frames from video footage, annotating them using LabelImg, and splitting the dataset into training, validation, and test sets.
* **Model Training:** Leveraging TensorFlow and TFLite to develop an optimized object detection model capable of real-time rabbit detection.
* **Deployment:** Testing the model on video data and building cross-platform applications for practical use, including macOS and Windows.

**Key Challenges and Solutions**

Major challenges included computational limitations, handling large datasets, and building platform-specific apps. These were addressed through platform selection (Google Colab Pro), leveraging university storage solutions, and cross-platform testing using Azure VMs and high-performance hardware.

**Current Achievements**

* The project is now deployed in a real-world setting, with the AI system actively used for monitoring rabbits in a farm environment.
* A fully functional app for both macOS and Windows has been delivered, facilitating easy use by non-technical stakeholders.

**Future Directions**

* **Model Enhancements:** Incorporate additional datasets and refine hyperparameters to further improve detection accuracy and efficiency.
* **Documentation and Open Source:** Finalize and upload the entire codebase with detailed documentation, demo videos, and GitHub integration for wider accessibility.
* **Scalability:** Explore cloud-based solutions to handle larger datasets and environments.

This project has been a remarkable learning journey, blending theoretical knowledge with practical challenges. By overcoming these challenges, the system is now a step closer to advancing the integration of AI into agricultural practices, paving the way for future innovations in automated animal monitoring.
