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Agriculture covers many crops, including\u00a0vegetables and fruit types which are grown under diverse conditions. Climate and soil type\u00a0are major factors during the\u00a0growing process\u00a0of potato. Another factor, the tuber itself, also\u00a0plays a crucial role in determining characteristics\u00a0of potato growth. It is therefore\u00a0beneficial to distinguish different types\u00a0of tubers, especially in the supply chain. Closer inspection of tubers\u00a0could serve as an effective method for analyzing the elements and minerals within the potato once tubers have been correctly separated. Using machine learning in this field significantly contributes to obtaining more advanced conclusions. The Visual Geometry Group made the VGG-19 model, which was used to sort different cultivars of potatoes. Transfer learning was used by stopping the last layer of the neural network. Going further, five types of meta-heuristic methods (GA, PSO, ABC, ALO, and GWO) were used to get the best hyperparameters that yielded better results than without using meta-heuristics. Artificial Bee Colony (ABC)\u00a0surpasses the\u00a0other methods as well as the standard approach by reaching 90% accuracy, which was better than the standard by 23% for six\u00a0cultivars\u00a0of potatoes on the test set while acquiring the best values.<\/jats:p>","DOI":"10.1007\/s11540-026-10057-4","type":"journal-article","created":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T13:55:39Z","timestamp":1793368539000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prowess of Meta-heuristics to Distinguish Potatoes"],"prefix":"10.1007","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8185-8924","authenticated-orcid":false,"given":"S\u00fcleyman","family":"\u00c7etiner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osman","family":"Altay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,30]]},"reference":[{"key":"10057_CR1","doi-asserted-by":"publisher","DOI":"10.22059\/poll.2024.380231.2490","author":"H Abdolabadi","year":"2024","unstructured":"Abdolabadi H, Khosravian E (2025) Improving the lifetime of wireless sensor networks for air quality monitoring using metaheuristic algorithms. 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