TY  - CONF
AU  - Cvejić, Sandra
AU  - Hrnjaković, Olivera
AU  - Jocković, Milan
AU  - Kupusinac, Aleksandar
AU  - Doroslovački, Ksenija
AU  - Radeka, Ilija
AU  - Jocić, Siniša
AU  - Miladinović, Dragana
AU  - Miklič, Vladimir
PY  - 2022
UR  - http://fiver.ifvcns.rs/handle/123456789/2889
AB  - Rapid innovation and liberalized market economy in agriculture require a fast and accurate Sunflower Oil Yield Prediction (SOYP). SOYP is a complex task since it depends on multiple factors. Machine Learning (ML) algorithms and the selection of important features could play a significant role in an accurate oil yield prediction. In this study, we developed ML models to predict oil yield by using two different sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared, were Artificial Neural Network, Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of 1250 samples of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. The results show that the RFR algorithm achieved the highest accuracy for both feature subsets. These research results indicate that ML has great potential for application as an alternative method for genotypic selection.
PB  - Paris : International Sunflower Association
PB  - Novi Sad : Institute of Field and Vegetable Crops
C3  - Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia
T1  - Feature selection and performance assessment of machine learning algorithms for sunflower oil yield prediction
EP  - 105
SP  - 105
UR  - https://hdl.handle.net/21.15107/rcub_fiver_2889
ER  - 

@conference{
author = "Cvejić, Sandra and Hrnjaković, Olivera and Jocković, Milan and Kupusinac, Aleksandar and Doroslovački, Ksenija and Radeka, Ilija and Jocić, Siniša and Miladinović, Dragana and Miklič, Vladimir",
year = "2022",
abstract = "Rapid innovation and liberalized market economy in agriculture require a fast and accurate Sunflower Oil Yield Prediction (SOYP). SOYP is a complex task since it depends on multiple factors. Machine Learning (ML) algorithms and the selection of important features could play a significant role in an accurate oil yield prediction. In this study, we developed ML models to predict oil yield by using two different sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared, were Artificial Neural Network, Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of 1250 samples of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. The results show that the RFR algorithm achieved the highest accuracy for both feature subsets. These research results indicate that ML has great potential for application as an alternative method for genotypic selection.",
publisher = "Paris : International Sunflower Association, Novi Sad : Institute of Field and Vegetable Crops",
journal = "Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia",
title = "Feature selection and performance assessment of machine learning algorithms for sunflower oil yield prediction",
pages = "105-105",
url = "https://hdl.handle.net/21.15107/rcub_fiver_2889"
}

Cvejić, S., Hrnjaković, O., Jocković, M., Kupusinac, A., Doroslovački, K., Radeka, I., Jocić, S., Miladinović, D.,& Miklič, V.. (2022). Feature selection and performance assessment of machine learning algorithms for sunflower oil yield prediction. in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia
Paris : International Sunflower Association., 105-105.
https://hdl.handle.net/21.15107/rcub_fiver_2889

Cvejić S, Hrnjaković O, Jocković M, Kupusinac A, Doroslovački K, Radeka I, Jocić S, Miladinović D, Miklič V. Feature selection and performance assessment of machine learning algorithms for sunflower oil yield prediction. in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia. 2022;:105-105.
https://hdl.handle.net/21.15107/rcub_fiver_2889 .

Cvejić, Sandra, Hrnjaković, Olivera, Jocković, Milan, Kupusinac, Aleksandar, Doroslovački, Ksenija, Radeka, Ilija, Jocić, Siniša, Miladinović, Dragana, Miklič, Vladimir, "Feature selection and performance assessment of machine learning algorithms for sunflower oil yield prediction" in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia (2022):105-105,
https://hdl.handle.net/21.15107/rcub_fiver_2889 .