Pourdarbani Razieh, Sabzi Sajad, Kalantari Davood, Hernández-Hernández José Luis, Arribas Juan Ignacio
Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181 68984, Iran.
Foods. 2020 Jan 21;9(2):113. doi: 10.3390/foods9020113.
Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert's judgment and assessment. Chickpea ( L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.
由于不同品种的作物有特定的用途，因此正确识别每个品种很重要，以避免假冒品种被当作真品出售，即欺诈行为。尽管训练有素的人类专家可能能够准确识别和分类作物品种，但由于疲劳、可重复性等因素会影响专家的判断和评估，所以仍需要计算机视觉系统。鹰嘴豆（L.）是一种在世界范围内重要的豆类，有多个品种。本文研究了三种外观颇为相似的鹰嘴豆品种：阿德尔、阿尔曼和阿扎德鹰嘴豆。本文的目的是提出一种用于自动分类这些鹰嘴豆品种的计算机视觉系统。首先，使用色调饱和度强度（HSI）颜色空间阈值进行分割。接下来，从鹰嘴豆样本图像中提取颜色和纹理（来自灰度共生矩阵，GLCM）属性（特征）。然后，使用混合人工神经网络 - 文化算法（ANN - CA），选择五个最有效属性（RGB颜色空间分量的均值、HSI颜色空间分量的均值、90°时GLCM矩阵的熵、0°时GLCM矩阵的标准差以及YCbCr颜色空间中的第三分量均值）的次优组合作为判别特征。最后，使用一种融合了三种不同分类器输出的人工神经网络 - 粒子群优化/蚁群优化/和声搜索多数投票（MV）集成方法，即混合人工神经网络 - 粒子群优化（ANN - PSO）、混合人工神经网络 - 蚁群优化（ANN - ACO）和混合人工神经网络 - 和声搜索（ANN - HS）。结果表明，在进行1000次随机迭代平均后，集成的人工神经网络 - PSO/ACO/HS - MV分类器方法在测试集上的平均分类准确率达到了99.10 ± 0.75%。
