Abstract The aim of this study was to identify the textural features of apple seeds with the highest discriminatory power for distinguishing the seeds of different apple cultivars with the use of discriminative classifiers. The seeds of apple cvs. Gala, Jonagold and Idared were scanned with the use of a flatbed scanner, and the acquired images were processed to calculate textural features from color channels: L, a, b, R, G, B, Y, U, V, H, S, I, X, Y and Z. The selected textures were used to develop discriminative models and distinguish the seeds of the examined apple cultivars. The analyses were performed for color spaces and color channels. The seeds of apple cvs. Gala and Idared were discriminated with 100% accuracy in models based on the textures from Lab and YUV color spaces and color channel L for the Naive Bayes, Multilayer Perceptron and Multi Class classifiers. The discriminatory accuracies of the seeds of all analyzed apple cultivars (Gala, Idared and Jonagold) ranged from 72% to 85%. The discriminatory accuracy of the textures selected from Lab color space for the Naive Bayes classifier reached 85%. The seeds of apple cvs. Gala and Jonagold were discriminated with 78–90% accuracy, and the discriminatory accuracy of the textures from Lab color space and color channel b for the Naive Bayes classifier reached 90%. The seeds of apple cvs. Idared and Jonagold were distinguished with 80–94% accuracy. The models based on textures from Lab color space and color channel b for the Naive Bayes classifier were characterized by 94% discriminatory accuracy. The study demonstrated that textural features are useful for discriminating the seeds of different apple cultivars.

中文翻译：

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利用种子纹理特征区分不同贮藏苹果品种

摘要 本研究的目的是利用判别分类器识别具有最高判别力的苹果种子的质地特征，以区分不同苹果品种的种子。苹果cvs的种子。Gala、Jonagold 和 Idared 使用平板扫描仪进行扫描，对获取的图像进行处理以计算来自颜色通道的纹理特征：L、a、b、R、G、B、Y、U、V、H、S 、I、X、Y 和 Z。选定的纹理用于开发判别模型并区分所检查的苹果品种的种子。对色彩空间和色彩通道进行了分析。苹果cvs的种子。Gala 和 Idared 在基于 Lab 和 YUV 颜色空间的纹理以及朴素贝叶斯的颜色通道 L 的模型中以 100% 的准确率被区分，多层感知器和多类分类器。所有分析的苹果品种（Gala、Idared 和 Jonagold）种子的判别准确度在 72% 到 85% 之间。朴素贝叶斯分类器从Lab颜色空间选取的纹理的判别准确率达到了85%。苹果cvs的种子。Gala 和 Jonagold 的判别准确率为 78-90%，而朴素贝叶斯分类器对 Lab 颜色空间和颜色通道 b 纹理的判别准确率达到了 90%。苹果cvs的种子。Idared 和 Jonagold 的准确率分别为 80-94%。基于来自 Lab 颜色空间的纹理和用于朴素贝叶斯分类器的颜色通道 b 的模型具有 94% 的判别准确率。