Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features

Erhan Akyol, Bilal Alatas and Inanc Ozgen ()
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Erhan Akyol: Department of Software Engineering, Firat University, Elazig 23119, Türkiye
Bilal Alatas: Department of Software Engineering, Firat University, Elazig 23119, Türkiye
Inanc Ozgen: Department of Bioengineering, Firat University, Elazig 23119, Türkiye

Agriculture, 2025, vol. 15, issue 21, 1-22

Abstract: The cherry fruit fly ( Rhagoletis cerasi L.) poses a major threat to global cherry production, with significant economic implications. This study presents an innovative approach to assist pest control strategies by classifying cherry fruit samples based on pomological data using evolutionary rule-based classification algorithms. A unique dataset comprising 396 samples from five different coloring periods was collected, focusing particularly on the second pomological period when pest activity peaks. Three evolutionary algorithms, CORE (Evolutionary Rule Extractor for Classification), DMEL (Data Mining with Evolutionary Learning for Classification) and OCEC (Organizational Evolutionary Classification), were applied to find interpretable classification rules that find whether an incoming cherry sample belongs to the second pomological period or other periods. Two distinct fitness functions were used to evaluate the algorithms’ performance. The results of the algorithms are compared with various visual graphs and the metric values are compared with visual graphs in a similar fashion. The findings highlight the potential of explainable AI models in enhancing agricultural decision-making and offer a novel, data-based methodology for integrated pest management in cherry production for the prediction of cherry fruit phenology class.

Keywords: Rhagoletis cerasi; pomological data; evolutionary algorithms; classification; rule-based learning; explainable AI (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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