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DATASETS See the articles for more detailed information on the data. |
ALL PUBLICATION LISTS |
Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Pistachio Image Dataset |
2 Class |
Classification Clustering |
Image |
2148 |
Image |
2022 |
Download |
Citation Request |
1: 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) DOI: https://doi.org/10.3390/electronics11070981 2: OZKAN IA., KOKLU M. and SARACOGLU R. (2021). Classification of Pistachio Species Using Improved K-NN Classifier. Progress in Nutrition, Vol. 23, N. 2. https://doi.org/10.23751/pn.v23i2.9686. (Open Access) |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Acoustic Extinguisher Fire Dataset |
2 Class |
Classification Clustering |
Integer, Real |
17.442 |
6 |
2022 |
Download |
Citation Request |
1: KOKLU M., TASPINAR Y.S., (2021). Determining the Extinguishing Status of Fuel Flames With Sound Wave by Machine Learning Methods. IEEE Access, 9, pp.86207-86216, Doi: 10.1109/ACCESS.2021.3088612 DOI: https://doi.org/10.1109/ACCESS.2021.3088612 2: TASPINAR Y.S., KOKLU M., ALTIN M., (2021). Classification of Flame Extinction Based on Acoustic Oscillations using Artificial Intelligence Methods. Case Studies in Thermal Engineering, 28, 101561, Doi: 10.1016/j.csite.2021.101561 DOI: https://doi.org/10.1016/j.csite.2021.101561 3: TASPINAR Y.S., KOKLU M., ALTIN M., (2022). Acoustic-Driven Airflow Flame Extinguishing System Design and Analysis of Capabilities of Low Frequency in Different Fuels. Fire Technology, Doi: 10.1007/s10694-021-01208-9 |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Grapevine Leaves Image Dataset |
5 Class |
Classification |
Image |
500 |
Image |
2022 |
Download |
Citation Request |
KOKLU, M., UNLERSEN, M. F., OZKAN, IA., ASLAN, M. F. and SABANCI, K. (2022). A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement, 188, 110425. |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Date Fruit Datasets |
7 Class |
Classification Clustering |
Integer, Real |
898 |
34 |
2021 |
Download |
Citation Request |
KOKLU, M., KURSUN, R., TASPINAR, Y. S. and CINAR, I. (2021). Classification of Date Fruits into Genetic Varieties Using Image Analysis. Mathematical Problems in Engineering, Vol.2021, Article ID: 4793293. |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Pumpkin Seeds Dataset |
2 Class |
Classification Clustering |
Integer, Real |
2500 |
12 |
2021 |
Download |
Citation Request |
KOKLU, M., SARIGIL, S. and OZBEK, O. (2021). The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.). Genetic Resources and Crop Evolution, 68(7), 2713-2726. doi: |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Pistachio Dataset |
2 Class |
Classification Clustering |
Integer, Real |
2148 |
16 |
2021 |
Download |
Citation Request |
OZKAN IA., KOKLU M. and SARACOGLU R. (2021). Classification of Pistachio Species Using Improved K-NN Classifier. Progress in Nutrition, Vol. 23, N. 2. https://doi.org/10.23751/pn.v23i2.9686. |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Rice MSC Dataset |
5 Class |
Classification Clustering |
Integer, Real |
75.000 |
106 |
2021 |
Download |
Citation Request |
1: KOKLU, M., CINAR, I. and TASPINAR, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. DOI: https://doi.org/10.1016/j.compag.2021.106285 2: CINAR, I. and KOKLU, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229-243. DOI: https://doi.org/10.15316/SJAFS.2021.252 3: CINAR, I. and KOKLU, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences, 28 (2), 307-325. DOI: https://doi.org/10.15832/ankutbd.862482 4: CINAR, I. and KOKLU, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Rice Image Dataset |
5 Class |
Classification |
Image |
75.000 |
Image |
2021 |
Download |
Citation Request |
1: KOKLU, M., CINAR, I. and TASPINAR, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. DOI: https://doi.org/10.1016/j.compag.2021.106285 2: CINAR, I. and KOKLU, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229-243. DOI: https://doi.org/10.15316/SJAFS.2021.252 3: CINAR, I. and KOKLU, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences, 28 (2), 307-325. DOI: https://doi.org/10.15832/ankutbd.862482 4: CINAR, I. and KOKLU, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Dry Bean Dataset |
7 Class |
Classification Clustering |
Integer, Real |
13.611 |
16 |
2020 |
|
Citation Request |
KOKLU, M. and OZKAN, I.A., (2020), “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.” Computers and Electronics in Agriculture, 174, 105507. DOI: https://doi.org/10.1016/j.compag.2020.105507 UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Raisin Dataset |
2 Class |
Classification Clustering |
Integer, Real |
900 |
7 |
2020 |
Download |
Citation Request |
CINAR I., KOKLU M. and TASDEMIR S., (2020), “Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods”, Gazi Journal of Engineering Sciences, vol. 6, no. 3, pp. 200-209, December, 2020. DOI: https://doi.org/10.30855/gmbd.2020.03.03 UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Raisin+Dataset |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Rice Dataset Cammeo and Osmancik |
2 Class |
Classification Clustering |
Integer, Real |
3810 |
9 |
2019 |
Download |
Citation Request |
CINAR, I. and KOKLU, M., (2019). “Classification of Rice Varieties Using Artificial Intelligence Methods.” International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. DOI: https://doi.org/10.18201/ijisae.2019355381 UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Rice+%28Cammeo+and+Osmancik%29 |
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Name |
Data Types |
Default Task |
Attribute Types |
# Instances |
# Attributes |
Year |
Download |
Durum Wheat Dataset |
3 Class |
Classification Clustering |
Image, Video, Real |
9000 |
236 |
2019 |
Download |
Citation Request |
KAYA, E., & SARITAS, I. (2019). Towards a real-time sorting system: identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Computers and Electronics in Agriculture, 166, 105016. |
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