Predicting Honey Production using Data Mining and Artificial Neural Network Algorithms in Apiculture
Predicting Honey Production using Data Mining and Artificial Neural Network Algorithms in Apiculture
Koksal Karadas1* and Ibrahim Hakki Kadırhanogullari2
ABSTRACT
This survey was conducted on all the 85 beekeeping farms collected with census study method in Igdir province of Turkey with the purpose of determining some factors influencing average honey yield (AHY) per beehive in the year 2014. For this purpose, predictive performances of several data mining algorithms (CART, CHAID, Exhaustive CHAID and MARS) and artificial neural network algorithm (Multilayer Perceptron, MLP) were evaluated comparatively. Several factors thought as independent variables in the survey were age of beekeeper (AB), education level (EL), number of full beehives (NFB), bee race (BR), the time spent in plateau (TSP), feed of autumn and spring (FAS), working period in apiculture during year (WPA), frequency of changing queen (FCQ), and controlling beehives in summer (CFB), respectively. Minimum beekeeping farm numbers for parent and child nodes were arranged as 8:4 in CART, CHAID and Exhaustive CHAID for attaining the best predictive performance in AHY. In the Exhaustive CHAID, only 3 independent variables, NFB, WPA and CFB were found statistically. In the CART algorithm, only NFB, WPA and AB independent variables were found significantly. In the MARS algorithm, significant independent variables were determined to be some main and interaction effects of NFB, FAS, WPA, EL, AB, FCQ and TSP. The significant order of the Pearson coefficients between actual and fitted values in AHY was MARS (0.913a) > ANN (0.885ab) > Exhaustive CHAID (0.786b) > CHAID (0.769b) > CART (0.744). It was concluded that the MARS algorithm having the best predictive accuracy among all the algorithms might offer a good solution to beekeepers in describing interactions of significant independent variables.
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October 2017
Vol. 49, Iss. 5, Pages 1547-1936
