| dc.description.abstract | Poultry Keeping substantially contributes to household food security and nutritional development and also helps diversify the income returns of people around East Arica. In Uganda, the poultry sector has more than 47 million chickens though the biggest percentage is taken up by local breeds. Egg production is estimated at 907.1 million with an increment of 2.8 percent from 882.6 million in 2016 as it contributes to 4.3% of annual agriculture revenue from the poultry sector.
However, a significant egg count inefficiency has been noticed by several farmers on their poultry farms resulting in fewer returns on investment. With this project, addressing this issue was the main agenda to comfort the farmers and have increased egg production on poultry farms around Uganda.
There would be increased egg production revenue collection if all the challenges, particularly theft, for our project as the already existing egg collection techniques, are modified with our new system.
The objective of this project was to design and develop a low-cost IoT and AI-based system to detect any egg laid by a poultry bird on a farm. Having broken down this main objective into three specific objectives, the project was implemented with the aid of a prototype that consisted of two systems namely; the raspberry pi 4 with components connected to it and the cloud server control system. The Pi camera module was connected to the raspberry pi 4 microprocessor to capture frames of eggs laid and send them in the form of bytes to the cloud server for processing by the Roboflow-trained model. In addition to this, the 16* 2 LCD was connected to the raspberry pi 4 to display the count of any egg laid from the data sent to the cloud server. Next was the cloud server system processed data received from the raspberry pi 4 by the Roboflow-trained model that was exported there in the virtual environment. Also, the same LCD count was obtained on a webpage via a hosted website, this ensured real-time happenings data access to the farmers remotely from their poultry farms.
As a result, this provided a more comprehensive view of egg production and also scaled up to larger farms maintaining the same level of accuracy. The project outcomes aimed at enhancing the farm's resilience, managing egg production, and inefficiencies, reduce on resource wastage, and improving profitability due to accurate monitoring which was successfully achieved. | en_US |
