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[J]. 《营养进展》, 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的开心果品种分类与分析[J]. 《电子学》, 11(7), 981. 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，用于区分两类具有不同特征、适配不同市场需求的开心果品种。采用高分辨率相机采集了两类共2148张开心果样本图像。对采集到的开心果样本图像实施图像处理、图像分割与特征提取操作，构建了包含16个属性的开心果数据集。基于简单高效的k近邻分类方法与主成分分析（Principal Component Analysis, PCA），针对所构建的数据集设计了一种进阶分类模型。本研究提出了一套涵盖特征提取、维度降维与维度加权三个阶段的多级分类系统。实验结果表明，所提方法的分类准确率达94.18%。本研究提出的高性能分类模型可满足开心果品种区分的重要应用需求，提升品种经济价值；此外，所开发的模型可推广至同类研究，具有重要的应用价值。
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
本研究通过计算机视觉系统采集了基利姆齐（Kirmizi）与锡尔特（Siirt）两类开心果的图像。本次实验所用的数据集共包含2148张图像，其中基利姆齐品种1232张、锡尔特品种916张。本研究采用三种不同的卷积神经网络（Convolutional Neural Network, CNN）模型对上述图像开展分类任务：基于迁移学习（Transfer Learning）方法，分别使用AlexNet以及预训练模型VGG16、VGG19完成模型训练。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分数指标对模型性能进行评估，同时结合受试者工作特征（Receiver Operating Characteristic, ROC）曲线与曲线下面积（Area Under Curve, AUC）值开展性能验证。其中VGG16模型取得了最高的分类准确率。实验结果证明，上述方法可成功应用于开心果品种鉴定。（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

搜集汇总

背景与挑战

背景概述

该数据集包含2148张两种开心果（Kirmizi和Siirt）的高分辨率图像，已成功应用于计算机视觉和深度学习研究，最高分类准确率达98.84%。数据集通过图像处理技术区分开心果品种，对提升农业经济价值具有重要意义。