Prediction of sugar beet yield and quality parameters with varying nitrogen fertilization using ensemble decision trees and artificial neural networks
•Sugar beet nitrogen fertilization was optimized using leaf nutrient status samples.•Root yield and yield quality parameters were used as training and test data.•Ensemble decision trees and artificial neural network regression were used.•Na had high variable importance for root yield and sucrose con...
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| Published in | Computers and electronics in agriculture Vol. 212; p. 108076 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0168-1699 1872-7107 |
| DOI | 10.1016/j.compag.2023.108076 |
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| Abstract | •Sugar beet nitrogen fertilization was optimized using leaf nutrient status samples.•Root yield and yield quality parameters were used as training and test data.•Ensemble decision trees and artificial neural network regression were used.•Na had high variable importance for root yield and sucrose content.•The top accuracy had a median R2 of 0.927 and ranged from 0.842 to 0.998.
Nitrogen fertilization has a crucial role in sugar beet production, especially concerning root yield and quality. This study employed a machine learning approach to predict root yield and quality parameters based on the nutrient status of sugar beet leaves in relation to nitrogen fertilization. The field experiment included the following N fertilization treatments for sugar beet production: control (N0), presowing (N1 = 45 kg N ha−1) and presowing with top-dressing (N2 = 99 and 154.5 kg ha−1). Leaf samples were collected during the vegetation period in six intervals (May-Sept) to determine the levels of N, K and Na in the leaf dry matter. The machine learning regression based on ensemble decision trees and artificial neural network was used to determine the relationship of leaf samples based on varying N fertilization with yield parameters. Among the leaf elements analyzed, Na exhibited the highest average relative variable importance for root yield, sucrose content, and other quality parameters during the season with greater precipitation. In the season with less precipitation, N content at the beginning of July showed higher importance on root yield (74.6). The evaluated machine learning methods consistently achieved high accuracy across various combinations of input data and yield parameters, with a median R2 of 0.927 and a range from 0.842 to 0.998. |
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| AbstractList | •Sugar beet nitrogen fertilization was optimized using leaf nutrient status samples.•Root yield and yield quality parameters were used as training and test data.•Ensemble decision trees and artificial neural network regression were used.•Na had high variable importance for root yield and sucrose content.•The top accuracy had a median R2 of 0.927 and ranged from 0.842 to 0.998.
Nitrogen fertilization has a crucial role in sugar beet production, especially concerning root yield and quality. This study employed a machine learning approach to predict root yield and quality parameters based on the nutrient status of sugar beet leaves in relation to nitrogen fertilization. The field experiment included the following N fertilization treatments for sugar beet production: control (N0), presowing (N1 = 45 kg N ha−1) and presowing with top-dressing (N2 = 99 and 154.5 kg ha−1). Leaf samples were collected during the vegetation period in six intervals (May-Sept) to determine the levels of N, K and Na in the leaf dry matter. The machine learning regression based on ensemble decision trees and artificial neural network was used to determine the relationship of leaf samples based on varying N fertilization with yield parameters. Among the leaf elements analyzed, Na exhibited the highest average relative variable importance for root yield, sucrose content, and other quality parameters during the season with greater precipitation. In the season with less precipitation, N content at the beginning of July showed higher importance on root yield (74.6). The evaluated machine learning methods consistently achieved high accuracy across various combinations of input data and yield parameters, with a median R2 of 0.927 and a range from 0.842 to 0.998. |
| ArticleNumber | 108076 |
| Author | Radočaj, Dorijan Jurišić, Mladen Markulj Kulundžić, Antonela Antunović, Manda Varga, Ivana |
| Author_xml | – sequence: 1 givenname: Ivana surname: Varga fullname: Varga, Ivana organization: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Department of Plant Production and Biotechnology, Vladimira Preloga 1, 31000 Osijek, Croatia – sequence: 2 givenname: Dorijan surname: Radočaj fullname: Radočaj, Dorijan email: dradocaj@fazos.hr organization: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Department of Agricultural Engineering and Renewable Energy Sources, Vladimira Preloga 1, 31000 Osijek, Croatia – sequence: 3 givenname: Mladen surname: Jurišić fullname: Jurišić, Mladen organization: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Department of Agricultural Engineering and Renewable Energy Sources, Vladimira Preloga 1, 31000 Osijek, Croatia – sequence: 4 givenname: Antonela surname: Markulj Kulundžić fullname: Markulj Kulundžić, Antonela organization: Agricultural Institute Osijek, Department of Breeding and Genetics of Industrial Plants, Južno predgrađe 17, 31 000 Osijek, Croatia – sequence: 5 givenname: Manda surname: Antunović fullname: Antunović, Manda organization: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Department of Plant Production and Biotechnology, Vladimira Preloga 1, 31000 Osijek, Croatia |
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| Cites_doi | 10.1016/j.compag.2018.10.024 10.3390/rs13040641 10.3390/nitrogen3020013 10.1109/ICAIIC57133.2023.10067006 10.1016/j.indcrop.2021.113753 10.1016/j.agwat.2020.106090 10.3390/plants11131697 10.3390/rs14010120 10.18047/poljo.21.2.3 10.3390/plants11121593 10.3390/s20041231 10.1111/pce.12815 10.3390/w13091308 10.3390/plants11151923 10.1016/j.indcrop.2022.114801 10.1094/PDIS-09-22-2156-RE 10.1016/j.rse.2017.06.043 10.1016/j.neunet.2020.10.002 10.1016/j.agwat.2015.05.005 10.3390/rs14092256 10.1016/j.compag.2018.05.012 10.3390/agronomy12092210 10.1002/agg2.20337 10.1007/s11356-021-16265-4 10.32615/ps.2020.036 10.1016/j.fcr.2022.108640 10.3390/rs11111318 10.1017/S0021859607006922 10.1016/j.eja.2006.06.004 10.1016/j.atech.2022.100102 10.1016/j.indcrop.2022.115762 10.1017/S0021859600017706 10.1016/j.indcrop.2018.02.051 10.1016/j.fcr.2007.04.001 10.1016/j.compag.2017.08.024 10.18047/poljo.26.1.5 |
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| Keywords | Precipitation Accuracy assessment Leaf samples Sugar beet root yield Machine learning |
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| References | ICUMSA Methods Book, 2007a. Determination of α-Amino Nitrogen in Sugar Beet by the Copper Method (‘Blue Number’) (Methods GS6-5); Bartens: Berlin, Germany. Bergmann (b0020) 1992 Pospišil (b0220) 2013 Draycott, Marsh, Tinker (b0070) 1970; 74 Hainmueller, J., Hazlett, C. 2017. KRLS: Kernel-Based Regularized Least Squares. https://CRAN.R-project.org/package=KRLS. Hesami, Pepe, Monthony, Baiton, Jones (b0110) 2021; 170 Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T. 2022. caret: Classification and Regression Training. https://CRAN.R-project.org/package=caret. Tao, Feng, Xu, Miao, Yang, Yang, Fan (b0250) 2020; 20 Elavarasan, Vincent, Sharma, Zomaya, Srinivasan (b0075) 2018; 155 Varga, Kerovec, Engler, Popović, Lončarić, Iljkić, Zebec, Antunović (b0270) 2022; 138 Khan, Kamaruddin, Ullah Sheikh, Zawawi, Yusup, Bakht, Mohamed Noor (b0135) 2022; 11 Lundegårdh (b0170) 1966 Bojtor, Mousavi, Illés, Golzardi, Széles, Szabó, Nagy, Marton (b0025) 2022; 11 Pejak, Lugonja, Antić, Panić, Pandžić, Alexakis, Mavrepis, Zhou, Marko, Crnojević (b0210) 2022; 14 Aasim, Katırcı, Akgur, Yildirim, Mustafa, Nadeem, Baloch, Karakoy, Yılmaz (b0010) 2022; 181 Mäck, Hoffmann (b0180) 2006; 25 Wang, Chang, Ma (b0285) 2022; 29 Li, Wei, Wang, Li, Huang, Xu, Xu (b0160) 2021; 13 Määttä, Bazaliy, Kimari, Djurabekova, Nordlund, Roos (b0175) 2021; 133 Tsiligaridis, J., 2023. Tree-Based Ensemble Models and Algorithms for Classification, in: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Presented at the 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 103–106. 10.1109/ICAIIC57133.2023.10067006. Radočaj, Jurišić (b0230) 2022; 12 Venkataraju, Arumugam, Stepan, Kiran, Peters (b0275) 2023; 3 Kristek, Kristek, Varga, Drmić (b0145) 2015; 21 Inoue, Y., Guérif, M., Baret, F., Skidmore, A., Gitelson, A., Schlerf, M., Darvishadeh, R., Olioso, A., 2016. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation, 39, 2609-2623). 10.1111/pce.12815. Brédy, Gallichand, Celicourt, Gumiere (b0030) 2020; 233 Tan, Hou, Wu, Du, Chen (b0245) 2019; 11 Nayak, Silva, Parihar, Krupnik, Sena, Kakraliya, Sahay Jat, Simgh Sidhu, Sharma, Lal Jat, Sapkota (b0200) 2022; 287 Liland, K. H., Mevik, B. H., Wehrens, R., Hiemstra, P., 2022. pls: Partial Least Squares and Principal Component Regression. https://CRAN.R-project.org/package=pls . Walsh, Nambi, Shafian, Jayawardena, Ansah, Lamichhane, McClintick-Chess (b0280) 2023; 6 Mäck, Hoffmann, Märländer (b0185) 2007; 102 Buchholz, Märländer, Puke, Glattkowski, Thielecke (b0040) 1995; 120 Pevalek-Kozlina (b0215) 2003 Pulkrábek, Brinar, Javor, Dvorak, Beckova, Kuchtová, Hubácková (b0225) 2021; 137 Fei, Fan, Fan, Xu (b0080) 2020; 58 Guan, Wu, Kimball, Anderson, Frolking, Li, Hain, Lobell (b0100) 2017; 199 Li, Jákli, Lu, Ren, Ming, Liu, Wang, Lu (b0155) 2018; 116 Pavlů, Chochola (b0205) 2021; 137 ICUMSA Methods Book, 2007 b. Determination of Potassium and Sodium in Sugar Beet by Flame Photometry (Methods GS6-7); Bartens: Berlin, Germany. Jaggard, Qi, Semenov (b0120) 2007; 145 Gilbertson, Van Niekerk (b0095) 2017; 142 Khalifani, Darvishzadeh, Azad, Rahmani (b0130) 2022; 189 Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., Yuan, J. 2022. xgboost: Extreme Gradient Boosting. https://CRAN.R-project.org/package=xgboost. Kiymaz, Ertek (b0140) 2015; 158 Barzin, Lotfi, Varco, Bora (b0015) 2021; 14 Mahajan, Das, Murgaokar, Herrmann, Berger, Sahoo, Patel, Desai, Morajkar, Kulkarni (b0190) 2021; 13 Breiman., L., Cutler, A., Liaw, A., Wiener, M. 2022. randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. https://CRAN.R-project.org/package=randomForest. Fugate, Khan, Eide, Hakk, Lafta, Qi (b0085) 2023; 107 Varga, Lončarić, Pospišil, Rastija, Antunović (b0260) 2020; 26 Dong, Wu, Liu, Fan, Leng, Yang (b0055) 2020; 123 (b0065) 2003 Varga, Jović, Rastija, Markulj Kulundžić, Zebec, Lončarić, Iljklić, Antunović (b0265) 2022; 3 Karatzoglou, A., Smola, A., Hornik, K., Maniscalco, M. A., Teo, C. H. 2022. kernlab: Kernel-Based Machine Learning Lab. https://CRAN.R-project.org/package=kernlab . Drachovská, Šandera (b0060) 1959 Chlingaryan, Sukkarieh, Whelan (b0050) 2018; 151 Ge, Zhao, Yu, Liu, Zhang, Gong, Sun (b0090) 2022; 11 Dong (10.1016/j.compag.2023.108076_b0055) 2020; 123 Wang (10.1016/j.compag.2023.108076_b0285) 2022; 29 Chlingaryan (10.1016/j.compag.2023.108076_b0050) 2018; 151 Brédy (10.1016/j.compag.2023.108076_b0030) 2020; 233 Pospišil (10.1016/j.compag.2023.108076_b0220) 2013 Pavlů (10.1016/j.compag.2023.108076_b0205) 2021; 137 Pevalek-Kozlina (10.1016/j.compag.2023.108076_b0215) 2003 Khalifani (10.1016/j.compag.2023.108076_b0130) 2022; 189 Elavarasan (10.1016/j.compag.2023.108076_b0075) 2018; 155 Ge (10.1016/j.compag.2023.108076_b0090) 2022; 11 Venkataraju (10.1016/j.compag.2023.108076_b0275) 2023; 3 Tao (10.1016/j.compag.2023.108076_b0250) 2020; 20 Li (10.1016/j.compag.2023.108076_b0160) 2021; 13 Varga (10.1016/j.compag.2023.108076_b0265) 2022; 3 Fei (10.1016/j.compag.2023.108076_b0080) 2020; 58 Varga (10.1016/j.compag.2023.108076_b0270) 2022; 138 10.1016/j.compag.2023.108076_b0105 Khan (10.1016/j.compag.2023.108076_b0135) 2022; 11 Aasim (10.1016/j.compag.2023.108076_b0010) 2022; 181 Bojtor (10.1016/j.compag.2023.108076_b0025) 2022; 11 Hesami (10.1016/j.compag.2023.108076_b0110) 2021; 170 10.1016/j.compag.2023.108076_b0150 Mäck (10.1016/j.compag.2023.108076_b0180) 2006; 25 Pulkrábek (10.1016/j.compag.2023.108076_b0225) 2021; 137 Kiymaz (10.1016/j.compag.2023.108076_b0140) 2015; 158 10.1016/j.compag.2023.108076_b0195 Buchholz (10.1016/j.compag.2023.108076_b0040) 1995; 120 Barzin (10.1016/j.compag.2023.108076_b0015) 2021; 14 Draycott (10.1016/j.compag.2023.108076_b0070) 1970; 74 10.1016/j.compag.2023.108076_b0035 Määttä (10.1016/j.compag.2023.108076_b0175) 2021; 133 10.1016/j.compag.2023.108076_b0115 10.1016/j.compag.2023.108076_b0165 Li (10.1016/j.compag.2023.108076_b0155) 2018; 116 Walsh (10.1016/j.compag.2023.108076_b0280) 2023; 6 Kristek (10.1016/j.compag.2023.108076_b0145) 2015; 21 Varga (10.1016/j.compag.2023.108076_b0260) 2020; 26 Tan (10.1016/j.compag.2023.108076_b0245) 2019; 11 Pejak (10.1016/j.compag.2023.108076_b0210) 2022; 14 10.1016/j.compag.2023.108076_b0125 Bergmann (10.1016/j.compag.2023.108076_b0020) 1992 Drachovská (10.1016/j.compag.2023.108076_b0060) 1959 10.1016/j.compag.2023.108076_b0045 Fugate (10.1016/j.compag.2023.108076_b0085) 2023; 107 10.1016/j.compag.2023.108076_b0005 Nayak (10.1016/j.compag.2023.108076_b0200) 2022; 287 Jaggard (10.1016/j.compag.2023.108076_b0120) 2007; 145 Mäck (10.1016/j.compag.2023.108076_b0185) 2007; 102 Radočaj (10.1016/j.compag.2023.108076_b0230) 2022; 12 Gilbertson (10.1016/j.compag.2023.108076_b0095) 2017; 142 Guan (10.1016/j.compag.2023.108076_b0100) 2017; 199 Lundegårdh (10.1016/j.compag.2023.108076_b0170) 1966 10.1016/j.compag.2023.108076_b0255 (10.1016/j.compag.2023.108076_b0065) 2003 Mahajan (10.1016/j.compag.2023.108076_b0190) 2021; 13 |
| References_xml | – reference: Tsiligaridis, J., 2023. Tree-Based Ensemble Models and Algorithms for Classification, in: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Presented at the 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 103–106. 10.1109/ICAIIC57133.2023.10067006. – volume: 137 start-page: 245 year: 2021 end-page: 247 ident: b0205 article-title: Short Study on Sugar Beet Nitrogen Fertilization – Local versus Uniform Nitrogen Application publication-title: Listy Cukrovarnické a Reparské – volume: 133 start-page: 123 year: 2021 end-page: 131 ident: b0175 article-title: Gradient-based training and pruning of radial basis function networks with an application in materials physics publication-title: Neural Netw. – volume: 11 start-page: 1593 year: 2022 ident: b0025 article-title: Nutrient Composition Analysis of Maize Hybrids Affected by Different Nitrogen Fertilisation Systems publication-title: Plants – volume: 29 start-page: 7014 year: 2022 end-page: 7024 ident: b0285 article-title: Predicting flocculant dosage in the drinking water treatment process using Elman neural network publication-title: Environ. Sci. Pollut. Res. – reference: Karatzoglou, A., Smola, A., Hornik, K., Maniscalco, M. A., Teo, C. H. 2022. kernlab: Kernel-Based Machine Learning Lab. https://CRAN.R-project.org/package=kernlab . – year: 2003 ident: b0215 article-title: Fiziologija bilja – volume: 181 year: 2022 ident: b0010 article-title: Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp ( publication-title: Ind. Crop. Prod. – volume: 14 start-page: 120 year: 2021 ident: b0015 article-title: Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield publication-title: Remote Sens. (Basel) – volume: 74 start-page: 567 year: 1970 end-page: 573 ident: b0070 article-title: Sodium and potassium relationships in sugar beet publication-title: J. Agric. Sci. – volume: 142 start-page: 50 year: 2017 end-page: 58 ident: b0095 article-title: Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning publication-title: Comput. Electron. Agric. – volume: 21 start-page: 15 year: 2015 end-page: 22 ident: b0145 article-title: Results of sugar beet production depending on the hybrids selection and the number of fungicide application publication-title: Poljoprivreda – reference: Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., Yuan, J. 2022. xgboost: Extreme Gradient Boosting. https://CRAN.R-project.org/package=xgboost. – year: 1992 ident: b0020 article-title: Nutritional Disorders of Plants – Development, visual and analytical diagnosis – volume: 138 start-page: 69 year: 2022 end-page: 72 ident: b0270 article-title: Determination of N-NO publication-title: Listy cukrovarnicke a reparske – volume: 14 start-page: 2256 year: 2022 ident: b0210 article-title: Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data publication-title: Remote Sens. (Basel) – volume: 199 start-page: 333 year: 2017 end-page: 349 ident: b0100 article-title: The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields publication-title: Remote Sens. Environ. – volume: 123 start-page: 49 year: 2020 end-page: 73 ident: b0055 article-title: Simulation of daily diffuse solar radiation based on three machine learning models publication-title: Comput. Model. Eng. Sci. – volume: 12 start-page: 2210 year: 2022 ident: b0230 article-title: GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production publication-title: Agronomy – volume: 11 start-page: 1318 year: 2019 ident: b0245 article-title: Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation publication-title: Remote Sens. (Basel) – volume: 107 start-page: 1816 year: 2023 end-page: 1821 ident: b0085 article-title: Sugar beet root storage properties are unaffected by Cercospora leaf spot publication-title: Plant Dis. – volume: 25 start-page: 270 year: 2006 end-page: 279 ident: b0180 article-title: Organ-specific adaptation to low precipitation in solute concentration of sugar beet ( publication-title: Eur. J. Agron. – reference: ICUMSA Methods Book, 2007a. Determination of α-Amino Nitrogen in Sugar Beet by the Copper Method (‘Blue Number’) (Methods GS6-5); Bartens: Berlin, Germany. – volume: 13 start-page: 1308 year: 2021 ident: b0160 article-title: A medium and Long-Term runoff forecast method based on massive meteorological data and machine learning algorithms publication-title: Water – volume: 158 start-page: 225 year: 2015 end-page: 234 ident: b0140 article-title: Water use and yield of sugar beet ( publication-title: Agric. Water Manag. – volume: 3 start-page: 170 year: 2022 end-page: 185 ident: b0265 article-title: Efficiency and Management of Nitrogen Fertilization in Sugar Beet as Spring Crop: A Review publication-title: Nitrogen – volume: 189 year: 2022 ident: b0130 article-title: Prediction of sunflower grain yield under normal and salinity stress by RBF-K, MLP and CNN models publication-title: Ind. Crops Prod. – volume: 3 start-page: 100102 year: 2023 ident: b0275 article-title: A Review of Machine Learning Techniques for Identifying Weeds in Corn publication-title: Smart Agric. Technol. – volume: 58 start-page: 869 year: 2020 end-page: 872 ident: b0080 article-title: Estimation of total nitrogen content in sugar beet leaves based on chlorophyll fluorescence parameters publication-title: Photosynthetica – volume: 20 start-page: 1231 year: 2020 ident: b0250 article-title: Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images publication-title: Sensors – volume: 11 start-page: 1697 year: 2022 ident: b0135 article-title: Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow publication-title: Plants – volume: 120 start-page: 113 year: 1995 end-page: 121 ident: b0040 article-title: Neubewertung des technischen Wertes von Zucker-rüben publication-title: Zuckerindustrie – volume: 155 start-page: 257 year: 2018 end-page: 282 ident: b0075 article-title: Forecasting yield by integrating agrarian factors and machine learning models: A survey publication-title: Comput. Electron. Agric. – reference: Hainmueller, J., Hazlett, C. 2017. KRLS: Kernel-Based Regularized Least Squares. https://CRAN.R-project.org/package=KRLS. – reference: Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T. 2022. caret: Classification and Regression Training. https://CRAN.R-project.org/package=caret. – volume: 170 year: 2021 ident: b0110 article-title: Modeling and optimizing in vitro seed germination of industrial hemp ( publication-title: Ind. Crop. Prod. – volume: 287 year: 2022 ident: b0200 article-title: Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India publication-title: Field Crop Res – volume: 13 start-page: 641 year: 2021 ident: b0190 article-title: Monitoring the foliar nutrients status of mango using spectroscopy-based spectral indices and PLSR-combined machine learning models publication-title: Remote Sens. (Basel) – volume: 26 start-page: 32 year: 2020 end-page: 39 ident: b0260 article-title: Dynamics of sugar beet root, crown and leaves mass with regard to plant densities and spring nitrogen fertilization publication-title: Poljoprivreda – reference: ICUMSA Methods Book, 2007 b. Determination of Potassium and Sodium in Sugar Beet by Flame Photometry (Methods GS6-7); Bartens: Berlin, Germany. – year: 1959 ident: b0060 article-title: Fysiologie cukrovky – volume: 145 start-page: 367 year: 2007 end-page: 375 ident: b0120 article-title: The impact of climate change on sugarbeet yield in the UK: 1976–2004 publication-title: J. Agric. Sci. – year: 2003 ident: b0065 publication-title: Nutrients for sugar beet production: soil-plant relationships – reference: Breiman., L., Cutler, A., Liaw, A., Wiener, M. 2022. randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. https://CRAN.R-project.org/package=randomForest. – volume: 11 start-page: 1923 year: 2022 ident: b0090 article-title: Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model publication-title: Plants – year: 1966 ident: b0170 article-title: Plant phisiology – volume: 137 start-page: 184 year: 2021 end-page: 193 ident: b0225 article-title: Experience with variable fertilization of sugar beet publication-title: Listy cukrovarnicke a reparske – volume: 233 year: 2020 ident: b0030 article-title: Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches publication-title: Agric. Water Manag. – reference: Inoue, Y., Guérif, M., Baret, F., Skidmore, A., Gitelson, A., Schlerf, M., Darvishadeh, R., Olioso, A., 2016. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation, 39, 2609-2623). 10.1111/pce.12815. – volume: 151 start-page: 61 year: 2018 end-page: 69 ident: b0050 article-title: Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review publication-title: Comput. Electron. Agric. – reference: Liland, K. H., Mevik, B. H., Wehrens, R., Hiemstra, P., 2022. pls: Partial Least Squares and Principal Component Regression. https://CRAN.R-project.org/package=pls . – volume: 102 start-page: 210 year: 2007 end-page: 218 ident: b0185 article-title: Nitrogen compounds in organs of two sugar beet genotypes ( publication-title: Field Crop Res – year: 2013 ident: b0220 article-title: Ratarstvo II. dio – Industrijsko bilje – volume: 6 start-page: e20337 year: 2023 ident: b0280 article-title: UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates publication-title: Agrosyst., Geosci. Environ. – volume: 116 start-page: 1 year: 2018 end-page: 14 ident: b0155 article-title: Assessing leaf nitrogen concentration of winter oilseed rape with canopy hyperspectral technique considering a non-uniform vertical nitrogen distribution publication-title: Ind. Crops Prod. – volume: 137 start-page: 245 year: 2021 ident: 10.1016/j.compag.2023.108076_b0205 article-title: Short Study on Sugar Beet Nitrogen Fertilization – Local versus Uniform Nitrogen Application publication-title: Listy Cukrovarnické a Reparské – volume: 155 start-page: 257 year: 2018 ident: 10.1016/j.compag.2023.108076_b0075 article-title: Forecasting yield by integrating agrarian factors and machine learning models: A survey publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.10.024 – year: 2003 ident: 10.1016/j.compag.2023.108076_b0215 – volume: 13 start-page: 641 year: 2021 ident: 10.1016/j.compag.2023.108076_b0190 article-title: Monitoring the foliar nutrients status of mango using spectroscopy-based spectral indices and PLSR-combined machine learning models publication-title: Remote Sens. (Basel) doi: 10.3390/rs13040641 – volume: 138 start-page: 69 year: 2022 ident: 10.1016/j.compag.2023.108076_b0270 article-title: Determination of N-NO3– in Sugar Beet Leaves publication-title: Listy cukrovarnicke a reparske – volume: 3 start-page: 170 year: 2022 ident: 10.1016/j.compag.2023.108076_b0265 article-title: Efficiency and Management of Nitrogen Fertilization in Sugar Beet as Spring Crop: A Review publication-title: Nitrogen doi: 10.3390/nitrogen3020013 – year: 1966 ident: 10.1016/j.compag.2023.108076_b0170 – ident: 10.1016/j.compag.2023.108076_b0255 doi: 10.1109/ICAIIC57133.2023.10067006 – volume: 170 year: 2021 ident: 10.1016/j.compag.2023.108076_b0110 article-title: Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.) publication-title: Ind. Crop. Prod. doi: 10.1016/j.indcrop.2021.113753 – volume: 120 start-page: 113 year: 1995 ident: 10.1016/j.compag.2023.108076_b0040 article-title: Neubewertung des technischen Wertes von Zucker-rüben publication-title: Zuckerindustrie – volume: 233 year: 2020 ident: 10.1016/j.compag.2023.108076_b0030 article-title: Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2020.106090 – volume: 11 start-page: 1697 issue: 13 year: 2022 ident: 10.1016/j.compag.2023.108076_b0135 article-title: Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow publication-title: Plants doi: 10.3390/plants11131697 – ident: 10.1016/j.compag.2023.108076_b0045 – volume: 14 start-page: 120 year: 2021 ident: 10.1016/j.compag.2023.108076_b0015 article-title: Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield publication-title: Remote Sens. (Basel) doi: 10.3390/rs14010120 – year: 2003 ident: 10.1016/j.compag.2023.108076_b0065 – ident: 10.1016/j.compag.2023.108076_b0125 – ident: 10.1016/j.compag.2023.108076_b0150 – volume: 137 start-page: 184 year: 2021 ident: 10.1016/j.compag.2023.108076_b0225 article-title: Experience with variable fertilization of sugar beet publication-title: Listy cukrovarnicke a reparske – volume: 123 start-page: 49 issue: 1 year: 2020 ident: 10.1016/j.compag.2023.108076_b0055 article-title: Simulation of daily diffuse solar radiation based on three machine learning models publication-title: Comput. Model. Eng. Sci. – volume: 21 start-page: 15 year: 2015 ident: 10.1016/j.compag.2023.108076_b0145 article-title: Results of sugar beet production depending on the hybrids selection and the number of fungicide application publication-title: Poljoprivreda doi: 10.18047/poljo.21.2.3 – volume: 11 start-page: 1593 year: 2022 ident: 10.1016/j.compag.2023.108076_b0025 article-title: Nutrient Composition Analysis of Maize Hybrids Affected by Different Nitrogen Fertilisation Systems publication-title: Plants doi: 10.3390/plants11121593 – year: 1992 ident: 10.1016/j.compag.2023.108076_b0020 – volume: 20 start-page: 1231 issue: 4 year: 2020 ident: 10.1016/j.compag.2023.108076_b0250 article-title: Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images publication-title: Sensors doi: 10.3390/s20041231 – ident: 10.1016/j.compag.2023.108076_b0115 doi: 10.1111/pce.12815 – volume: 13 start-page: 1308 year: 2021 ident: 10.1016/j.compag.2023.108076_b0160 article-title: A medium and Long-Term runoff forecast method based on massive meteorological data and machine learning algorithms publication-title: Water doi: 10.3390/w13091308 – volume: 11 start-page: 1923 issue: 15 year: 2022 ident: 10.1016/j.compag.2023.108076_b0090 article-title: Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model publication-title: Plants doi: 10.3390/plants11151923 – volume: 181 year: 2022 ident: 10.1016/j.compag.2023.108076_b0010 article-title: Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.) publication-title: Ind. Crop. Prod. doi: 10.1016/j.indcrop.2022.114801 – volume: 107 start-page: 1816 issue: 6 year: 2023 ident: 10.1016/j.compag.2023.108076_b0085 article-title: Sugar beet root storage properties are unaffected by Cercospora leaf spot publication-title: Plant Dis. doi: 10.1094/PDIS-09-22-2156-RE – volume: 199 start-page: 333 year: 2017 ident: 10.1016/j.compag.2023.108076_b0100 article-title: The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.043 – ident: 10.1016/j.compag.2023.108076_b0195 – volume: 133 start-page: 123 year: 2021 ident: 10.1016/j.compag.2023.108076_b0175 article-title: Gradient-based training and pruning of radial basis function networks with an application in materials physics publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.10.002 – volume: 158 start-page: 225 year: 2015 ident: 10.1016/j.compag.2023.108076_b0140 article-title: Water use and yield of sugar beet (Beta vulgaris L.) under drip irrigation at different water regimes publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2015.05.005 – volume: 14 start-page: 2256 year: 2022 ident: 10.1016/j.compag.2023.108076_b0210 article-title: Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data publication-title: Remote Sens. (Basel) doi: 10.3390/rs14092256 – volume: 151 start-page: 61 year: 2018 ident: 10.1016/j.compag.2023.108076_b0050 article-title: Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.05.012 – ident: 10.1016/j.compag.2023.108076_b0105 – volume: 12 start-page: 2210 year: 2022 ident: 10.1016/j.compag.2023.108076_b0230 article-title: GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production publication-title: Agronomy doi: 10.3390/agronomy12092210 – year: 1959 ident: 10.1016/j.compag.2023.108076_b0060 – volume: 6 start-page: e20337 issue: 1 year: 2023 ident: 10.1016/j.compag.2023.108076_b0280 article-title: UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates publication-title: Agrosyst., Geosci. Environ. doi: 10.1002/agg2.20337 – volume: 29 start-page: 7014 issue: 5 year: 2022 ident: 10.1016/j.compag.2023.108076_b0285 article-title: Predicting flocculant dosage in the drinking water treatment process using Elman neural network publication-title: Environ. Sci. Pollut. Res. doi: 10.1007/s11356-021-16265-4 – volume: 58 start-page: 869 issue: 3 year: 2020 ident: 10.1016/j.compag.2023.108076_b0080 article-title: Estimation of total nitrogen content in sugar beet leaves based on chlorophyll fluorescence parameters publication-title: Photosynthetica doi: 10.32615/ps.2020.036 – ident: 10.1016/j.compag.2023.108076_b0005 – volume: 287 year: 2022 ident: 10.1016/j.compag.2023.108076_b0200 article-title: Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India publication-title: Field Crop Res doi: 10.1016/j.fcr.2022.108640 – volume: 11 start-page: 1318 issue: 11 year: 2019 ident: 10.1016/j.compag.2023.108076_b0245 article-title: Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation publication-title: Remote Sens. (Basel) doi: 10.3390/rs11111318 – year: 2013 ident: 10.1016/j.compag.2023.108076_b0220 – volume: 145 start-page: 367 year: 2007 ident: 10.1016/j.compag.2023.108076_b0120 article-title: The impact of climate change on sugarbeet yield in the UK: 1976–2004 publication-title: J. Agric. Sci. doi: 10.1017/S0021859607006922 – volume: 25 start-page: 270 year: 2006 ident: 10.1016/j.compag.2023.108076_b0180 article-title: Organ-specific adaptation to low precipitation in solute concentration of sugar beet (Beta vulgaris L.) publication-title: Eur. J. Agron. doi: 10.1016/j.eja.2006.06.004 – volume: 3 start-page: 100102 year: 2023 ident: 10.1016/j.compag.2023.108076_b0275 article-title: A Review of Machine Learning Techniques for Identifying Weeds in Corn publication-title: Smart Agric. Technol. doi: 10.1016/j.atech.2022.100102 – volume: 189 year: 2022 ident: 10.1016/j.compag.2023.108076_b0130 article-title: Prediction of sunflower grain yield under normal and salinity stress by RBF-K, MLP and CNN models publication-title: Ind. Crops Prod. doi: 10.1016/j.indcrop.2022.115762 – ident: 10.1016/j.compag.2023.108076_b0165 – volume: 74 start-page: 567 issue: 3 year: 1970 ident: 10.1016/j.compag.2023.108076_b0070 article-title: Sodium and potassium relationships in sugar beet publication-title: J. Agric. Sci. doi: 10.1017/S0021859600017706 – ident: 10.1016/j.compag.2023.108076_b0035 – volume: 116 start-page: 1 year: 2018 ident: 10.1016/j.compag.2023.108076_b0155 article-title: Assessing leaf nitrogen concentration of winter oilseed rape with canopy hyperspectral technique considering a non-uniform vertical nitrogen distribution publication-title: Ind. Crops Prod. doi: 10.1016/j.indcrop.2018.02.051 – volume: 102 start-page: 210 issue: 3 year: 2007 ident: 10.1016/j.compag.2023.108076_b0185 article-title: Nitrogen compounds in organs of two sugar beet genotypes (Beta vulgaris L.) during the season publication-title: Field Crop Res doi: 10.1016/j.fcr.2007.04.001 – volume: 142 start-page: 50 year: 2017 ident: 10.1016/j.compag.2023.108076_b0095 article-title: Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.08.024 – volume: 26 start-page: 32 year: 2020 ident: 10.1016/j.compag.2023.108076_b0260 article-title: Dynamics of sugar beet root, crown and leaves mass with regard to plant densities and spring nitrogen fertilization publication-title: Poljoprivreda doi: 10.18047/poljo.26.1.5 |
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| SubjectTerms | Accuracy assessment Leaf samples Machine learning Precipitation Sugar beet root yield |
| Title | Prediction of sugar beet yield and quality parameters with varying nitrogen fertilization using ensemble decision trees and artificial neural networks |
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