Intelligent Mobile-Based Detection of Shrimp Weight Anomalies Using Random Forest Regression
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Abstract
Shrimp is one of the most widely consumed seafood items globally, yet consumers frequently encounter fraud, such as weight manipulation through adulteration injections, which poses significant health and economic risks. This research presents a practical system for detecting anomalies in shrimp weight. A cross-platform mobile application has been developed to classify shrimp as either normal or abnormal in weight. The application integrates a shrimp segmentation model, developed using Mask R-CNN, and a weight prediction model based on the random forest algorithm, utilizing features such as area, perimeter, length, and width of the shrimp image. The weight prediction model achieves a value of 0.821 and a Mean Absolute Error (MAE) of 1.786 grams, which is less than 10% of the average shrimp weight in the dataset. Final classification is performed by comparing the predicted weight with the actual weight, measured using a 7-segment digit recognition module. The developed mobile application represents a novel integration of machine learning with mobile technology to address both non-adulterated and adulterated shrimp scenarios. It offers a reliable, accessible tool for consumers to detect weight-based adulteration, thereby helping to mitigate health risks and economic losses in the seafood supply chain.
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