Introduction
Background and Context
In modern agricultural practices, especially in animal husbandry, monitoring the health and population of livestock is crucial for efficient farm management. Traditional methods of counting and monitoring animals are often labor-intensive, time-consuming, and prone to human error. In the context of rabbit farming, these challenges are magnified due to the rabbits' small size and agility. Automated monitoring systems leveraging advancements in artificial intelligence (AI) and computer vision can significantly alleviate these challenges, offering precise and real-time data collection.
The use of AI for animal detection and counting has gained substantial interest in recent years. By deploying deep learning models, farms can benefit from continuous, accurate monitoring, which is essential for maintaining animal health, optimizing feeding schedules, and managing farm resources effectively. This project focuses on developing an AI-based system for detecting and counting rabbits in a farm environment using video footage from CCTV cameras.
Below are samples of the raw video footage captured from the rabbit farm's CCTV cameras. These videos highlight the natural farm environment and the challenges encountered in rabbit detection, such as overlapping individuals, rapid movement, and varying lighting conditions. These challenges formed the basis of this project and motivated the development of an automated AI system:
This project is entirely self-developed, with all datasets, annotations, and implementations conducted from scratch. Extensive experimentation, including tuning hyperparameters and testing different object detection models, was carried out to achieve optimal performance.
Objectives and Goals
The primary objective of this project is to create an automated system that can detect and count rabbits in real-time from video footage. The specific goals include:
Data Collection: Gather video footage from CCTV cameras installed in a rabbit farm.
Data Annotation: Convert video footage into frames and annotate these frames to create a labeled dataset.
Model Training: Train a deep learning-based object detection model using the annotated dataset.
Model Evaluation: Evaluate the model's performance using appropriate metrics to ensure accuracy and reliability.
Real-time Detection: Implement the trained model to perform real-time rabbit detection and counting from video streams.
Reporting: Develop a system to log the number of rabbits detected per frame and generate reports.
The workflow of this project consists of collecting video data from a rabbit farm, annotating images to create a labeled dataset, training a deep learning model using TensorFlow, and deploying the model into a user-friendly application capable of processing live videos and generating reports on detected rabbits. This end-to-end pipeline demonstrates the potential of AI to transform traditional agricultural practices.
Importance and Applications
The implementation of an automated rabbit detection and counting system offers several significant benefits:
Efficiency: Reduces the manual labor required for counting rabbits, allowing farm staff to focus on other essential tasks.
Accuracy: Provides precise and consistent counting, minimizing human errors and improving farm management decisions.
Real-time Monitoring: Enables continuous monitoring, which is vital for promptly identifying health issues or anomalies in the rabbit population.
Data-Driven Decisions: Facilitates data collection that can be used to analyze trends, optimize feeding schedules, and improve overall farm productivity.
This system has been successfully deployed in a rabbit farm under the Agricultural Department of our university. The system processes live video feeds from CCTV cameras, detecting and counting rabbits in real time, thereby reducing manual intervention and ensuring precise monitoring
By integrating AI technology into rabbit farming, this project aims to demonstrate the practical applications of deep learning in agriculture, paving the way for more intelligent and automated farm management systems.
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