<![CDATA[The broiler duck farming industry in Indonesia faces challenges in efficiently monitoring body weight, as traditional manual weighing methods are labor-intensive, time-consuming, and stressful for the animals. To address this issue, this study aims to develop a non-invasive and automated weight estimation system that integrates digital image processing, machine learning, and Internet of Things (IoT) technologies. The methodology involves acquiring multi-angle images of ducks, applying preprocessing steps such as resizing, normalization, and contrast enhancement, and extracting hand-crafted features, including Histogram of Oriented Gradients (HOG) and HSV color histograms. These features are then fused, reduced via Principal Component Analysis (PCA), and processed using a Support Vector Regression (SVR) model with optimized hyperparameters for weight prediction. While previous studies have focused on cattle, broilers, or fish, research specifically targeting meat-type ducks remains limited, particularly those that combine image-based regression with IoT-enabled real-time monitoring. Experimental results demonstrate that the proposed system achieves a mean absolute error (MAE) of approximately 110 grams on the validation set, with per-duck averaging improving stability compared to per-image predictions. Visualization through scatter plots, boxplots, and learning curves further confirms that the model effectively captures general weight distribution trends but exhibits higher errors in certain mid-weight ranges. The integration with IoT facilitates continuous, stress-free monitoring of duck growth, underscoring the system’s potential as a practical and sustainable solution for precision livestock farming.]]>
