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

引文请求：
1. OZKAN IA., KOKLU M. 和 SARACOGLU R.（2021）. 基于改进K-NN分类器的开心果物种分类. 营养学进展，第23卷，第2期，第9686页. 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）. 利用预训练深度学习模型对开心果物种进行分类与分析，电子学，第11卷，第7期，第981页. https://doi.org/10.3390/electronics11070981. （开放获取）
数据集：https://www.muratkoklu.com/datasets/

摘要：本研究开发了一套计算机视觉系统，用以区分具有不同特征、针对不同市场类型的两种不同物种的开心果。使用高分辨率相机采集了这两种开心果的2148个样本图像。对所获得的开心果样本图像应用了图像处理技术、分割和特征提取。创建了一个包含十六个属性的开心果数据集。在所获得的数据库上设计了一种基于k-NN方法的先进分类器，该方法是一种简单而有效的分类器，并辅以主成分分析。本研究提出了一种包括特征提取、降维和维度加权阶段的分级系统。实验结果表明，所提出的方法实现了94.18%的分类成功率。所展示的高性能分类模型对于开心果物种的分离具有重要意义，并提升了物种的经济价值。此外，该模型在应用于类似研究中亦具有重大意义。
关键词：分类，图像处理，k近邻分类器，开心果物种

在研究范围内，通过计算机视觉系统获取了Kirmizi和Siirt两种开心果类型的图像。预训练数据集包括总共2148个图像，其中Kirmizi类型1232个，Siirt类型916个。使用了三种不同的卷积神经网络模型对这些图像进行分类。模型通过迁移学习方法，使用AlexNet以及预训练模型VGG16和VGG19进行训练。数据集分为80%的培训和20%的测试。分类结果显示，AlexNet、VGG16和VGG19模型分别获得了94.42%、98.84%和98.14%的成功率。通过敏感性、特异性、精确度和F-1分数等指标评估了模型的性能。此外，还使用了ROC曲线和AUC值来评估性能。VGG16模型实现了最高的分类成功率。所获得的结果揭示了这些方法在开心果类型的确定中可以成功应用。
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

Pistachio Image Dataset是一个用于开心果物种分类的图像数据集，包含2148个高分辨率图像，涵盖Kirmizi和Siirt两种类型。数据集通过计算机视觉和图像处理技术提取16个特征，并应用k-NN分类器和预训练深度学习模型（如VGG16）实现高准确率分类，最高达98.84%，旨在提升物种分离的经济价值和推广到类似研究。