Application of meta-heuristic feature selection method in low-cost portable device for watermelon classification using signal processing techniques.

Recognizing the stage of fruit maturity and thus determining the optimum harvesting time is critical since the competitive market requires high-quality products at a competitive price. Furthermore, in the context of the global water crisis, harvesting watermelons at an inappropriate ripening stage causes water wasting of around 280kg per kilo of watermelon as a virtual water footprint, highlighting the necessity of criteria for harvesting ripe watermelons by the farmers. This research thus aims to classify the Charleston Gray watermelon type into three categories i.e. unripe, ripe, and overripe using portable acoustic signal processing, data mining methods, and artificial intelligence approaches. Signal processing in the time and frequency domains and Wavelet Transformation were used to extract essential features from acoustic signals of the watermelons. This was followed by the selection of the significant features in classification using a t-test mean comparison. Sample categorization was accomplished using Support Vector Machines and...