Almond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. The market value of almonds is determined according to the quality of the almonds. Manually determining the quality of almonds by humans is a prone to error, time-consuming, and tiring process. In this study, For this reasons, well-known twelve pre-trained CNNs were used to classify almonds as normal and damaged. Then, the most successful model was used as a feature extractor, and the features were classified with various machine learning algorithms. In addition to all these, features were selected by using the FPA algorithm, and the classification process was carried out. Experimental results showed that the use of CNNs as feature extractors and classification with machine learning algorithms can provide better results than the classical softmax structure. In addition, the proposed FPA-based feature extraction increases the classification performance.
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Yurdakul et al. (2024) studied this question.
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