Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach
Autori
Pezo, LatoLončar, Biljana
Šovljanski, Olja
Tomić, Ana
Travičić, Vanja
Pezo, Milada
Aćimović, Milica
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Province, Serbia) and fertilization type (six different treatments). The output variables were morphological and quality parameters, with agricultural importance such as plant height, umbel diameter, number of umbels, number of seeds per umbel, 1000-seed weight, seed yield per plant, plant weight, harvest index, yield per ha, essential oil (EO) yield, germination energy, total germination, EO content, as well as the share of EOs compounds, including limonene, cis-dihydro carvone, met...hyl chavicol, carvone, cis-anethole, trans-anethole, β-elemene, α-himachalene, trans-βfarnesene, γ-himachalene, trans-muurola-4(14),5-diene, α-zingiberene, β-himachalene, β-bisabolene, trans-pseudoisoeugenyl 2-methylbutyrate and epoxy-pseudoisoeugenyl 2-methylbutyrate. The ANN model predicted agricultural parameters accurately, showing r2 values between 0.555 and 0.918, while r2 values for the forecasting of essential oil content were between 0.379 and 0.908. According to global sensitivity analysis, the fertilization type was a more influential variable to agricultural parameters, while the location site was more influential to essential oils content.
Ključne reči:
aniseed / essential oils / growing year / locality / fertilization / artificial neural networks / anise / Pimpinella anisumIzvor:
Life (Basel), 2022, 12, 11, 1722-Izdavač:
- Basel : MDPI
Finansiranje / projekti:
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200051 (Institut za opštu i fizičku hemiju, Beograd) (RS-MESTD-inst-2020-200051)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200134 (Univerzitet u Novom Sadu, Tehnološki fakultet) (RS-MESTD-inst-2020-200134)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (RS-MESTD-inst-2020-200032)
Kolekcije
Institucija/grupa
FiVeRTY - JOUR AU - Pezo, Lato AU - Lončar, Biljana AU - Šovljanski, Olja AU - Tomić, Ana AU - Travičić, Vanja AU - Pezo, Milada AU - Aćimović, Milica PY - 2022 UR - http://fiver.ifvcns.rs/handle/123456789/3237 AB - Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Province, Serbia) and fertilization type (six different treatments). The output variables were morphological and quality parameters, with agricultural importance such as plant height, umbel diameter, number of umbels, number of seeds per umbel, 1000-seed weight, seed yield per plant, plant weight, harvest index, yield per ha, essential oil (EO) yield, germination energy, total germination, EO content, as well as the share of EOs compounds, including limonene, cis-dihydro carvone, methyl chavicol, carvone, cis-anethole, trans-anethole, β-elemene, α-himachalene, trans-βfarnesene, γ-himachalene, trans-muurola-4(14),5-diene, α-zingiberene, β-himachalene, β-bisabolene, trans-pseudoisoeugenyl 2-methylbutyrate and epoxy-pseudoisoeugenyl 2-methylbutyrate. The ANN model predicted agricultural parameters accurately, showing r2 values between 0.555 and 0.918, while r2 values for the forecasting of essential oil content were between 0.379 and 0.908. According to global sensitivity analysis, the fertilization type was a more influential variable to agricultural parameters, while the location site was more influential to essential oils content. PB - Basel : MDPI T2 - Life (Basel) T1 - Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach IS - 11 SP - 1722 VL - 12 DO - 10.3390/life12111722 ER -
@article{ author = "Pezo, Lato and Lončar, Biljana and Šovljanski, Olja and Tomić, Ana and Travičić, Vanja and Pezo, Milada and Aćimović, Milica", year = "2022", abstract = "Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Province, Serbia) and fertilization type (six different treatments). The output variables were morphological and quality parameters, with agricultural importance such as plant height, umbel diameter, number of umbels, number of seeds per umbel, 1000-seed weight, seed yield per plant, plant weight, harvest index, yield per ha, essential oil (EO) yield, germination energy, total germination, EO content, as well as the share of EOs compounds, including limonene, cis-dihydro carvone, methyl chavicol, carvone, cis-anethole, trans-anethole, β-elemene, α-himachalene, trans-βfarnesene, γ-himachalene, trans-muurola-4(14),5-diene, α-zingiberene, β-himachalene, β-bisabolene, trans-pseudoisoeugenyl 2-methylbutyrate and epoxy-pseudoisoeugenyl 2-methylbutyrate. The ANN model predicted agricultural parameters accurately, showing r2 values between 0.555 and 0.918, while r2 values for the forecasting of essential oil content were between 0.379 and 0.908. According to global sensitivity analysis, the fertilization type was a more influential variable to agricultural parameters, while the location site was more influential to essential oils content.", publisher = "Basel : MDPI", journal = "Life (Basel)", title = "Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach", number = "11", pages = "1722", volume = "12", doi = "10.3390/life12111722" }
Pezo, L., Lončar, B., Šovljanski, O., Tomić, A., Travičić, V., Pezo, M.,& Aćimović, M.. (2022). Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach. in Life (Basel) Basel : MDPI., 12(11), 1722. https://doi.org/10.3390/life12111722
Pezo L, Lončar B, Šovljanski O, Tomić A, Travičić V, Pezo M, Aćimović M. Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach. in Life (Basel). 2022;12(11):1722. doi:10.3390/life12111722 .
Pezo, Lato, Lončar, Biljana, Šovljanski, Olja, Tomić, Ana, Travičić, Vanja, Pezo, Milada, Aćimović, Milica, "Agricultural parameters and essential oil content composition prediction of aniseed, based on growing year, locality and fertilization type - an artificial neural network approach" in Life (Basel), 12, no. 11 (2022):1722, https://doi.org/10.3390/life12111722 . .