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Sorting and classification of fruits according to weight is traditionally done by hand. But human factors often cause error and inaccuracies. Automatically performing this process with machines is important in terms of speeding up the process, reducing costs, and minimizing errors. This research aimed to develop a machine-learning model to improve weight estimation of Carabao mangoes (Mangifera indica). To accomplish this, a conveyor belt system and web camera were employed to collect videos for training and validation purposes. Image processing techniques were utilized to extract and calculate pixel count, length, and width of the captured mangoes. The dataset comprised randomly selected carabao mangoes, each with pre-determined weights obtained through digital weighing scales. The developed machine learning Multiple Linear Regression (MLR) model employed a total of 106 training samples and achieved an accuracy of 92.44%, with an additional 106 validation samples attaining 97.32% accuracy.
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