Pistachio Image Dataset

Citation Request :
1. OZKAN IA., KOKLU M. and SARACOGLU R. (2021). Classification of Pistachio Species Using Improved K-NN Classifier. Progress in Nutrition, Vol. 23, N. 2, pp. DOI:10.23751/pn.v23i2.9686. (Open Access)
2. SINGH D, TASPINAR YS, KURSUN R, CINAR I, KOKLU M, OZKAN IA, LEE H-N., (2022). Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models, Electronics, 11 (7), 981. https://doi.org/10.3390/electronics11070981. (Open Access)
DATASET: https://www.muratkoklu.com/datasets/

https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178
ABSTRACT: A computer vision system has been developed to distinguish two different species of pistachios with different characteristics that address different market types. 2148 sample image for these two kinds of pistachios were taken with a high-resolution camera. The image processing techniques, segmentation and feature extraction were applied on the obtained images of the pistachio samples. A pistachio dataset that has sixteen attributes was created. An advanced classifier based on k-NN method, which is a simple and successful classifier, and principal component analysis was designed on the obtained dataset. In this study; a multi-level system including feature extraction, dimension reduction and dimension weighting stages has been proposed. Experimental results showed that the proposed approach achieved a classification success of 94.18%. The presented high-performance classification model provides an important need for the separation of pistachio species and increases the economic value of species. In addition, the developed model is important in terms of its application to similar studies.
Keywords: Classification, Image processing, k nearest neighbor classifier, Pistachio species

https://doi.org/10.3390/electronics11070981
Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types. View Full-Text
Keywords: pistachio; genetic varieties; machine learning; deep learning; food recognition

引用请求：
1. OZKAN IA、KOKLU M与SARACOGLU R.（2021）. 基于改进K近邻（k-Nearest Neighbor, K-NN）分类器的开心果品种分类. 《营养进展》（Progress in Nutrition），第23卷第2期，DOI:10.23751/pn.v23i2.9686. （开放获取）
2. SINGH D、TASPINAR YS、KURSUN R、CINAR I、KOKLU M、OZKAN IA、LEE H-N.（2022）. 基于预训练深度学习模型（pre-trained deep learning models）的开心果品种分类与分析，《电子学》（Electronics），第11卷第7期，第981页. https://doi.org/10.3390/electronics11070981. （开放获取）
数据集来源：https://www.muratkoklu.com/datasets/

https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178
摘要：本研究开发了一套计算机视觉系统（computer vision system），用于区分两类特征各异、适配不同市场需求的开心果品种。研究通过高分辨率相机采集了两类共2148张开心果样本图像，对获取的图像依次应用图像处理、分割与特征提取技术，最终构建了包含16个属性的开心果数据集。基于简单高效的K近邻（k-Nearest Neighbor, K-NN）方法与主成分分析（Principal Component Analysis, PCA），设计了一款进阶分类器。本研究提出了一套涵盖特征提取、降维与维度加权的多级分类框架。实验结果表明，所提方法的分类准确率达94.18%。该高性能分类模型可为开心果品种甄别提供关键技术支撑，有效提升相关品种的经济价值，其研究思路亦可作为同类研究的重要参考。
关键词：分类、图像处理、K近邻分类器、开心果品种

https://doi.org/10.3390/electronics11070981
摘要：本研究通过计算机视觉系统（computer vision system）采集了Kirmizi与Siirt两种开心果的图像，构建的预训练数据集共包含2148张样本图像，其中Kirmizi品种1232张、Siirt品种916张。研究采用三款不同的卷积神经网络（Convolutional Neural Network, CNN）模型开展图像分类任务，通过迁移学习（transfer learning）方法对AlexNet、VGG16与VGG19三款预训练模型进行训练。数据集按80%训练集、20%测试集的比例划分。分类实验结果显示，AlexNet、VGG16与VGG19模型的分类准确率分别为94.42%、98.84%与98.14%。研究通过灵敏度、特异度、精确率与F1值指标评估模型性能，并结合受试者工作特征（Receiver Operating Characteristic, ROC）曲线与AUC值开展性能验证。其中VGG16模型取得了最优分类准确率。研究结果证实，上述方法可成功应用于开心果品种识别任务。（查看全文）
关键词：开心果；遗传品种；机器学习；深度学习；食品识别