A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

Abstract

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.

Apricot Stone Classification Using Image Analysis and Machine Learning

“…For the models built based only on textures, size, or shape, the accuracies reached 77.3% using SVM, 57.3% using SVM, and 43.9% using RF, respectively [43]. Models based on textures extracted from images have been developed to distinguish cultivars of apricot stones ('Taja', 'Early Orange', 'Harcot', and 'Bella') [44]. The most useful machine learning algorithms were found to be IBk from Lazy, Multilayer Perceptron from Functions, and Random Forest from Trees.…”

“…The most useful machine learning algorithms were found to be IBk from Lazy, Multilayer Perceptron from Functions, and Random Forest from Trees. The average accuracy of discrimination reached 99% for the model built using the Multilayer Perceptron algorithm based on image textures from the Lab colour space [44]. In the case of stones and stones classification, the 'Royal Glory' and 'Redhaven' peach cultivars were discriminated with an accuracy of up to 100% with models including a set of selected textures of images from the following channel R, G, B, L, a, b, X, Y, Z built for stones using the Bayes Net algorithm, and for stones, Bayes Net, Logistic, Sequential Minimal Optimization (SMO), and multi-class classifier.…”

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

“…Classification using machine vision can result in higher accuracy and a reduction in time [31]. Many previous studies proved the effectiveness of distinguishing seed cultivars belonging to different species, e.g., peach [32], tomato [11], pepper [33], apricot [34], or apple [35,36] using an approach combining image analysis and traditional machine learning with an accuracy close to 90-100%. In addition, deep learning models provided very high correctness in the classification of seeds of pepper [19], maize [37], cotton [38], soybean [39], and weed seeds [40] reaching accuracy even close to 100%.…”