<![CDATA[Feed Conversion Ratio (FCR) is a widely recognized technique in the animal farming industry, especially for optimizing feed efficiency and reducing the operational costs. A key aspect of managing FCR involves achieving efficiency in the correlation between the number of animals and the required feed quantity. However, to achieve accurately counting large populations of animals, such as chickens, presents a significant challenge especially in large-scale farming. Computer vision technology offers a promising solution to automate this counting process, helping the FCR management. This research specifically evaluates the capability of the YOLOv11 model for real-time chicken detection. Evaluation of the model's performance indicates high efficacy, achieving accuracy, precision, and recall values of 93%, 94%, and 98%, respectively. The implementation of the technology for precise chicken detection facilitates the accurate adjustment and optimization of feed allocation, which can substantially enhance the overall FCR process.]]>
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