Yield and quality prediction of winter rapeseed — artificial neural network and random forest models
Authors
Rajković, DraganaMarjanović-Jeromela, Ana
Pezo, Lato
Lončar, Biljana
Zanetti, Federica
Monti, Andrea
Kondić-Špika, Ankica
Article (Published version)
Metadata
Show full item recordAbstract
As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype.... The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively).
Keywords:
rapeseed / yields / oil content / mathematical modelling / artificial neural network / random forest / yield prediction / oilseed rape / ANNs / andom forest regression / RFR / seed yield / oil yield / prediction capabilitiesSource:
Agronomy - Basel, 2022, 12, 58-Publisher:
- MDPI, Basel
Funding / projects:
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200051 (Institute of General and Physical Chemistry, Belgrade) (RS-MESTD-inst-2020-200051)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200134 (University of Novi Sad, Faculty of Technology) (RS-MESTD-inst-2020-200134)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200032 (Institute of Field and Vegetable Crops, Novi Sad) (RS-MESTD-inst-2020-200032)
DOI: 10.3390/agronomy12010058
ISSN: 2073-4395
WoS: 000749919500001
Scopus: 2-s2.0-85122182340
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Institution/Community
FiVeRTY - JOUR AU - Rajković, Dragana AU - Marjanović-Jeromela, Ana AU - Pezo, Lato AU - Lončar, Biljana AU - Zanetti, Federica AU - Monti, Andrea AU - Kondić-Špika, Ankica PY - 2022 UR - http://fiver.ifvcns.rs/handle/123456789/2478 AB - As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively). PB - MDPI, Basel T2 - Agronomy - Basel T1 - Yield and quality prediction of winter rapeseed — artificial neural network and random forest models SP - 58 VL - 12 DO - 10.3390/agronomy12010058 ER -
@article{ author = "Rajković, Dragana and Marjanović-Jeromela, Ana and Pezo, Lato and Lončar, Biljana and Zanetti, Federica and Monti, Andrea and Kondić-Špika, Ankica", year = "2022", abstract = "As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively).", publisher = "MDPI, Basel", journal = "Agronomy - Basel", title = "Yield and quality prediction of winter rapeseed — artificial neural network and random forest models", pages = "58", volume = "12", doi = "10.3390/agronomy12010058" }
Rajković, D., Marjanović-Jeromela, A., Pezo, L., Lončar, B., Zanetti, F., Monti, A.,& Kondić-Špika, A.. (2022). Yield and quality prediction of winter rapeseed — artificial neural network and random forest models. in Agronomy - Basel MDPI, Basel., 12, 58. https://doi.org/10.3390/agronomy12010058
Rajković D, Marjanović-Jeromela A, Pezo L, Lončar B, Zanetti F, Monti A, Kondić-Špika A. Yield and quality prediction of winter rapeseed — artificial neural network and random forest models. in Agronomy - Basel. 2022;12:58. doi:10.3390/agronomy12010058 .
Rajković, Dragana, Marjanović-Jeromela, Ana, Pezo, Lato, Lončar, Biljana, Zanetti, Federica, Monti, Andrea, Kondić-Špika, Ankica, "Yield and quality prediction of winter rapeseed — artificial neural network and random forest models" in Agronomy - Basel, 12 (2022):58, https://doi.org/10.3390/agronomy12010058 . .
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