Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining

Colour is an essential parameter at product quality control stages, and finally, it is necessary for the consumer marketing decision. It is possible to damage the products during the process from collection to storage. Also, it is a well-known condition, cold environmental conditions protect fruits from deformations negative effects, but most of the time, most of the consumers keep the fruits at room temperature in open packs during the consumption process. Also, this condition affects the product storage time. In this study, it is aimed that to determine the behaviours of the fruits in room temperature and humidity conditions. For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. For this purpose, 100 “Akça” pear and 100 “Deveci” local pear cultivar used for experiments. Fruits were equally damaged by using a pendulum mechanism. The damaged fruits were kept at room temperature. Colour change areas on fruits were evaluated with X-rite Ci60 spectrophotometer, and the hardness of fruits was measured by using a fruit penetrometer. The colour (L, a, b) and ΔE values were analysed for the fruit cultivars. The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. The best prediction were found at the Majority Voting method (MAVL) 98.458 % success given with 70% partitioning.