Estimation of the Weights of Almond Nuts Based on Physical Properties through Data Mining

Feyza GÜRBÜZ, Bünyamin DEMİR, İkbal ESKİ, Zeynel Abidin KUŞ, Kadir Uğurtan YILMAZ, Ertuğrul İLİKÇİOĞLU, Sezai ERCİŞLİ

Abstract


Quality attributes are the major parameters designating market values of the agricultural goods and commodities. Several practices are applied to improve quality parameters of the fruits and vegetables. Such quality attributes should also be estimated through various approaches before to design of equipment and tools used in handling and processing of these goods and to design storage facilities. Data mining is a novel approach used to estimate various attributes or quality parameters of the fruits from previously measured attributes. Different algorithms embedded into data mining operations may yield quite accurate and reliable equations for estimation of quality attributes. Almond is a significant cash crop for growers. Since almond is quite tolerant to droughts and salinity, it is preferred in various parts of the country by producers. Weight is the primary quality parameter designating market value of the almonds. This study was conducted to estimate nut weights of seven different almond varieties and to develop an equation for the estimation of nut weights. Data mining approach was used to estimate nut weights from physical fruit quality attributes (kernel length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, shape index and aspect ratio). Present findings revealed quite significant, accurate and practicable rules to estimate the nut weights of different almond varieties. It was concluded that data mining could be used as a reliable tool to estimate the nut weights of different almond varieties from the physical attributes of the fruits.


Keywords


find laws; fruit quality; fruit weights; polyanalyst; shape index

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References


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DOI: http://dx.doi.org/10.15835/nbha46210631

June 1, 2017: Notulae Botanicae Horti Agrobotanici Cluj-Napoca in CiteScore rank (Scopus – Elsevier) 28/66 in Horticulture
 
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