TY  - CONF
AU  - Cvejić, Sandra
AU  - Radanović, Aleksandra
AU  - Kondić-Špika, Ankica
AU  - Miladinović, Dragana
AU  - Luković, Jadranka
AU  - Jocković, Jelena
AU  - Dedić, Boško
AU  - Jocković, Milan
AU  - Hrnjaković, Olivera
AU  - Gvozdenac, Sonja
AU  - Ćuk, Nemanja
AU  - Hladni, Nada
AU  - Jocić, Siniša
PY  - 2023
UR  - http://fiver.ifvcns.rs/handle/123456789/3996
AB  - Klimatske promene negativno utiču na proizvodnju ratarskih useva, posebno tokom dugih sušnih perioda. Jedan od ključnih pristupa za ublažavanje uticaja ekstremnih klimatskih uslova je oplemenjivanje useva kako bi se poboljšala njihova prilagodljivost različitim uslovima gajenja. Međutim, tradicionalne metode oplemenjivanja su često dugotrajne, radno intenzivne i rezultati evaluacije mogu biti pristrasni. U cilju dubljeg razumevanja adaptivnog odgovora suncokreta na sušne stresove uzrokovane klimatskim promenama, SmartSun projekat primenjuje holistički pristup koji obuhvata digitalnu fenotipizaciju korena suncokreta, analizu epigenetskih mehanizama i integraciju dobijenih podataka mašinskim učenjem. Do sada je razvijen protokol za rani rast korena suncokreta kroz fenotipizaciju u rizotronima pod različitim uslovima zalivanja (70, 42 i 50% zapreminskog sadržaja vode). Testirane su inbred linije suncokreta sa Instituta za ratarstvo i povrtarstvo kako bi se identifikovali tolerantni i osetljivi genotipovi. Identifikovane su ključne morfološke osobine korena koje su pouzdane u razlikovanju osetljivosti na sušu.
AB  - Climate change has a negative impact on the production of field crops, especially during extended periods of drought. One of the key strategies to mitigate the effects of extreme climate conditions is crop breeding aimed at enhancing their adaptability to various growing conditions. However, traditional breeding methods are often time-consuming, labor-intensive, and the evaluation outcomes may be biased. To gain a deeper understanding of the adaptive response of sunflowers to drought stress caused by climate change, the SmartSun Project employs a holistic approach encompassing digital root phenotyping, analysis of epigenetic mechanisms, and the integration of acquired data through machine learning. A protocol for early sunflower root growth has been developed using phenotyping in rhizotrons under different watering conditions (70%, 42%, and 50% volumetric water content). Inbred sunflower lines from the Institute of Field and Vegetable Crops (IFVCNS) were tested to identify tolerant and sensitive genotypes. Key morphological root traits that reliably differentiate drought sensitivity have been identified.
PB  - Beograd : Društvo genetičara Srbije
PB  - Beograd : Društvo selekcionera i semenara Srbije
C3  - Zbornik apstrakata, 10. Simpozijum Društva selekcionera i semenara Republike Srbije i 7. Simpozijum Sekcije za oplemenjivanje organizama Društva genetičara Srbije, Vrnjačka Banja, 16-18.10.2023.
T1  - SmartSun projekat: multidisciplinarni pristup u potrazi za “klimatski pametnim” suncokretom
T1  - SmartSun project: a multidisciplinary approach for unveiling a “climate-smart” sunflower
EP  - 144
SP  - 143
UR  - https://hdl.handle.net/21.15107/rcub_fiver_3996
ER  - 

@conference{
author = "Cvejić, Sandra and Radanović, Aleksandra and Kondić-Špika, Ankica and Miladinović, Dragana and Luković, Jadranka and Jocković, Jelena and Dedić, Boško and Jocković, Milan and Hrnjaković, Olivera and Gvozdenac, Sonja and Ćuk, Nemanja and Hladni, Nada and Jocić, Siniša",
year = "2023",
abstract = "Klimatske promene negativno utiču na proizvodnju ratarskih useva, posebno tokom dugih sušnih perioda. Jedan od ključnih pristupa za ublažavanje uticaja ekstremnih klimatskih uslova je oplemenjivanje useva kako bi se poboljšala njihova prilagodljivost različitim uslovima gajenja. Međutim, tradicionalne metode oplemenjivanja su često dugotrajne, radno intenzivne i rezultati evaluacije mogu biti pristrasni. U cilju dubljeg razumevanja adaptivnog odgovora suncokreta na sušne stresove uzrokovane klimatskim promenama, SmartSun projekat primenjuje holistički pristup koji obuhvata digitalnu fenotipizaciju korena suncokreta, analizu epigenetskih mehanizama i integraciju dobijenih podataka mašinskim učenjem. Do sada je razvijen protokol za rani rast korena suncokreta kroz fenotipizaciju u rizotronima pod različitim uslovima zalivanja (70, 42 i 50% zapreminskog sadržaja vode). Testirane su inbred linije suncokreta sa Instituta za ratarstvo i povrtarstvo kako bi se identifikovali tolerantni i osetljivi genotipovi. Identifikovane su ključne morfološke osobine korena koje su pouzdane u razlikovanju osetljivosti na sušu., Climate change has a negative impact on the production of field crops, especially during extended periods of drought. One of the key strategies to mitigate the effects of extreme climate conditions is crop breeding aimed at enhancing their adaptability to various growing conditions. However, traditional breeding methods are often time-consuming, labor-intensive, and the evaluation outcomes may be biased. To gain a deeper understanding of the adaptive response of sunflowers to drought stress caused by climate change, the SmartSun Project employs a holistic approach encompassing digital root phenotyping, analysis of epigenetic mechanisms, and the integration of acquired data through machine learning. A protocol for early sunflower root growth has been developed using phenotyping in rhizotrons under different watering conditions (70%, 42%, and 50% volumetric water content). Inbred sunflower lines from the Institute of Field and Vegetable Crops (IFVCNS) were tested to identify tolerant and sensitive genotypes. Key morphological root traits that reliably differentiate drought sensitivity have been identified.",
publisher = "Beograd : Društvo genetičara Srbije, Beograd : Društvo selekcionera i semenara Srbije",
journal = "Zbornik apstrakata, 10. Simpozijum Društva selekcionera i semenara Republike Srbije i 7. Simpozijum Sekcije za oplemenjivanje organizama Društva genetičara Srbije, Vrnjačka Banja, 16-18.10.2023.",
title = "SmartSun projekat: multidisciplinarni pristup u potrazi za “klimatski pametnim” suncokretom, SmartSun project: a multidisciplinary approach for unveiling a “climate-smart” sunflower",
pages = "144-143",
url = "https://hdl.handle.net/21.15107/rcub_fiver_3996"
}

Cvejić, S., Radanović, A., Kondić-Špika, A., Miladinović, D., Luković, J., Jocković, J., Dedić, B., Jocković, M., Hrnjaković, O., Gvozdenac, S., Ćuk, N., Hladni, N.,& Jocić, S.. (2023). SmartSun projekat: multidisciplinarni pristup u potrazi za “klimatski pametnim” suncokretom. in Zbornik apstrakata, 10. Simpozijum Društva selekcionera i semenara Republike Srbije i 7. Simpozijum Sekcije za oplemenjivanje organizama Društva genetičara Srbije, Vrnjačka Banja, 16-18.10.2023.
Beograd : Društvo genetičara Srbije., 143-144.
https://hdl.handle.net/21.15107/rcub_fiver_3996

Cvejić S, Radanović A, Kondić-Špika A, Miladinović D, Luković J, Jocković J, Dedić B, Jocković M, Hrnjaković O, Gvozdenac S, Ćuk N, Hladni N, Jocić S. SmartSun projekat: multidisciplinarni pristup u potrazi za “klimatski pametnim” suncokretom. in Zbornik apstrakata, 10. Simpozijum Društva selekcionera i semenara Republike Srbije i 7. Simpozijum Sekcije za oplemenjivanje organizama Društva genetičara Srbije, Vrnjačka Banja, 16-18.10.2023.. 2023;:143-144.
https://hdl.handle.net/21.15107/rcub_fiver_3996 .

Cvejić, Sandra, Radanović, Aleksandra, Kondić-Špika, Ankica, Miladinović, Dragana, Luković, Jadranka, Jocković, Jelena, Dedić, Boško, Jocković, Milan, Hrnjaković, Olivera, Gvozdenac, Sonja, Ćuk, Nemanja, Hladni, Nada, Jocić, Siniša, "SmartSun projekat: multidisciplinarni pristup u potrazi za “klimatski pametnim” suncokretom" in Zbornik apstrakata, 10. Simpozijum Društva selekcionera i semenara Republike Srbije i 7. Simpozijum Sekcije za oplemenjivanje organizama Društva genetičara Srbije, Vrnjačka Banja, 16-18.10.2023. (2023):143-144,
https://hdl.handle.net/21.15107/rcub_fiver_3996 .

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 .

TY  - CONF
AU  - Radanović, Aleksandra
AU  - Cvejić, Sandra
AU  - Luković, Jadranka
AU  - Jocković, Milan
AU  - Jocić, Siniša
AU  - Dedić, Boško
AU  - Gvozdenac, Sonja
AU  - Ćuk, Nemanja
AU  - Hladni, Nada
AU  - Jocković, Jelena
AU  - Hrnjaković, Olivera
AU  - Miladinović, Dragana
PY  - 2022
UR  - http://fiver.ifvcns.rs/handle/123456789/2913
AB  - Among the effects of wide-ranging climate change, drought presents a significant threat to global agricultural production. Drought reduces yield quantity and quality of crops, particularly in the semiarid and arid regions. Better understanding of plant adaptation to drought is of great interest to both crop science and the society as a whole. Institute of Field and Vegetable Crops (IFVCNS) possesses one of the largest collections of different wild and cultivated sunflower genotypes and populations worldwide which will be exploited for analysis of drought tolerance mechanisms. Within the project SmartSun the holistic approach of phenotyping and epigenetic mechanisms which modulate sunflower adaptation to drought stresses driven by climate change will be exploited. Using statistical and machine learning techniques, SmartSun will link the data obtained from phenotyping and epiQTLs to create climate-smart sunflower genotypes that will respond to the variable, unstable, and drought environmental growth conditions.
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  - Creating climate smart sunflower for future challenges – the SMARTSUN multidisciplinary project
EP  - 252
SP  - 252
UR  - https://hdl.handle.net/21.15107/rcub_fiver_2913
ER  - 

@conference{
author = "Radanović, Aleksandra and Cvejić, Sandra and Luković, Jadranka and Jocković, Milan and Jocić, Siniša and Dedić, Boško and Gvozdenac, Sonja and Ćuk, Nemanja and Hladni, Nada and Jocković, Jelena and Hrnjaković, Olivera and Miladinović, Dragana",
year = "2022",
abstract = "Among the effects of wide-ranging climate change, drought presents a significant threat to global agricultural production. Drought reduces yield quantity and quality of crops, particularly in the semiarid and arid regions. Better understanding of plant adaptation to drought is of great interest to both crop science and the society as a whole. Institute of Field and Vegetable Crops (IFVCNS) possesses one of the largest collections of different wild and cultivated sunflower genotypes and populations worldwide which will be exploited for analysis of drought tolerance mechanisms. Within the project SmartSun the holistic approach of phenotyping and epigenetic mechanisms which modulate sunflower adaptation to drought stresses driven by climate change will be exploited. Using statistical and machine learning techniques, SmartSun will link the data obtained from phenotyping and epiQTLs to create climate-smart sunflower genotypes that will respond to the variable, unstable, and drought environmental growth conditions.",
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 = "Creating climate smart sunflower for future challenges – the SMARTSUN multidisciplinary project",
pages = "252-252",
url = "https://hdl.handle.net/21.15107/rcub_fiver_2913"
}

Radanović, A., Cvejić, S., Luković, J., Jocković, M., Jocić, S., Dedić, B., Gvozdenac, S., Ćuk, N., Hladni, N., Jocković, J., Hrnjaković, O.,& Miladinović, D.. (2022). Creating climate smart sunflower for future challenges – the SMARTSUN multidisciplinary project. in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia
Paris : International Sunflower Association., 252-252.
https://hdl.handle.net/21.15107/rcub_fiver_2913

Radanović A, Cvejić S, Luković J, Jocković M, Jocić S, Dedić B, Gvozdenac S, Ćuk N, Hladni N, Jocković J, Hrnjaković O, Miladinović D. Creating climate smart sunflower for future challenges – the SMARTSUN multidisciplinary project. in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia. 2022;:252-252.
https://hdl.handle.net/21.15107/rcub_fiver_2913 .

Radanović, Aleksandra, Cvejić, Sandra, Luković, Jadranka, Jocković, Milan, Jocić, Siniša, Dedić, Boško, Gvozdenac, Sonja, Ćuk, Nemanja, Hladni, Nada, Jocković, Jelena, Hrnjaković, Olivera, Miladinović, Dragana, "Creating climate smart sunflower for future challenges – the SMARTSUN multidisciplinary project" in Proceedings, 20th International Sunflower Conference, 20-23 June 2022, Novi Sad, Serbia (2022):252-252,
https://hdl.handle.net/21.15107/rcub_fiver_2913 .