Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning

Abstract

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars.

Enhancing Apple Cultivar Classification Using Multiview Images

“…This also shows the importance of cultivar classification. In addition to apples, studies include other fruit such as grapes [36,37], cherries [13], hazelnuts [10,11], or tomatoes [12]. Many works regarding apple cultivars have emerged over the past few years.…”

“…However, training sub-models per viewpoint results in higher memory requirements for the overall model, which might hinder its deployment on mobile devices in the field. Cultivar classification is starting to attract more attention for various crops (e.g., apple, hazelnuts [10,11], tomato [12], cherry [13]). These mostly rely on single-view models to classify rather well separable cultivars with only a few classes.…”

Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels

“…In the present research, a machine learning technology for plum cultivar discrimination from images of the fruit kernel was developed and practiced. The machine learning method depends on (I) the comprehensive information collection from the images where the kernel textural traits are shown into a big dataset, and (II) the intelligent analysis of the quantitative data via artificial intelligence using algorithms in a computer program like MaZda and WEKA [11]. As a result, the technological machine combines a variety of skills into a network system, like identifying visual signals, converting them into digital data, processing data with different algorithms, modeling the results via analysis, and finally predicting a correlative trendline between plum cultivars and the textural traits of plum kernels.…”

“…The application machine learning classifiers and features extracted from images obtained using a flatbed scanner allowed for the cultivar discrimination of sour cherry pits with accuracies reaching 96.25% for four cultivars and 100% for two cultivars [15] and in the case of images acquired using a digital camera-for distinguishing two cultivars of sweet cherry pits in 100% of cases and three cultivars in 98% [16], and the discrimination of two cultivars of peach stones and seeds with the accuracy of up to 100% [17]. The application of linear dimensions and shape factors for the development of models allowed for the discrimination of the pits belonging to different sour cherry cultivars with an accuracy of up to 96% [11]. The morphometric features extracted from digital images acquired using a flatbed scanner and stepwise linear discriminant analysis were used to compare the modern and archaeological Prunus fruit stones.…”

“…Correct cultivar identification can be necessary for the processing industry to avoid mixing kernel cultivars with different compositions. The cultivar recognition may also allow avoiding falsification of kernel cultivars and reject kernels with undesirable properties for further processing [11,15,16]. Therefore, the objective of this study was to develop models for distinguishing the plum kernel cultivars based on selected image texture parameters using various algorithms (classifiers).…”