Frost prediction using machine learning and deep neural network models
This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events...
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| Vydané v: | Frontiers in artificial intelligence Ročník 5; s. 963781 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Switzerland
Frontiers Media S.A
12.01.2023
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| ISSN: | 2624-8212, 2624-8212 |
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| Abstract | This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations. |
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| AbstractList | This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations. This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6-48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53-1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6-48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53-1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations. |
| Author | Talsma, Carl J. Mudunuru, Maruti K. Powell, Michelle R. Solander, Kurt C. Crawford, Brandon |
| AuthorAffiliation | 4 Los Alamos National Laboratory, Facility System Engineering Utilities and Infrastructure Division , Los Alamos, NM , United States 1 Los Alamos National Laboratory, Earth and Environmental Sciences Division , Los Alamos, NM , United States 2 Carbon Solutions LLC , Bloomington, IN , United States 3 Pacific Northwest National Laboratory, Watershed and Ecosystem Science , Richland, WA , United States |
| AuthorAffiliation_xml | – name: 4 Los Alamos National Laboratory, Facility System Engineering Utilities and Infrastructure Division , Los Alamos, NM , United States – name: 2 Carbon Solutions LLC , Bloomington, IN , United States – name: 1 Los Alamos National Laboratory, Earth and Environmental Sciences Division , Los Alamos, NM , United States – name: 3 Pacific Northwest National Laboratory, Watershed and Ecosystem Science , Richland, WA , United States |
| Author_xml | – sequence: 1 givenname: Carl J. surname: Talsma fullname: Talsma, Carl J. – sequence: 2 givenname: Kurt C. surname: Solander fullname: Solander, Kurt C. – sequence: 3 givenname: Maruti K. surname: Mudunuru fullname: Mudunuru, Maruti K. – sequence: 4 givenname: Brandon surname: Crawford fullname: Crawford, Brandon – sequence: 5 givenname: Michelle R. surname: Powell fullname: Powell, Michelle R. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36714205$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1922705$$D View this record in Osti.gov |
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| Cites_doi | 10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2 10.1109/SCISISIS50064.2020.9322770 10.1016/0020-7373(89)90027-8 10.1007/978-3-319-67585-5_69 10.1029/2008JD009879 10.3390/atmos12070846 10.5194/npg-14-211-2007 10.1002/joc.3370120602 10.1175/1520-0450(1982)021<0518:ATVOFF>2.0.CO;2 10.1175/WAF-D-10-05014.1 10.1890/12-0200.1 10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2 10.1186/1471-2105-10-213 10.1109/JIOT.2018.2867333 10.1002/2016JD025920 10.1155/2016/2075186 10.1023/A:1005202814071 10.1175/WAF-D-20-0098.1 10.1121/2.0000749 10.21236/ADA561957 10.1109/HPCSim.2016.7568428 10.1007/s10546-013-9885-z 10.1016/j.compag.2006.09.001 10.1007/s11749-016-0481-7 10.1023/A:1010933404324 10.1007/978-3-319-99462-8_4 10.1175/WAF-D-21-0151.1 10.1016/j.scienta.2015.12.003 10.1088/1755-1315/191/1/012015 10.1016/j.compag.2008.05.019 10.1175/WAF-D-21-0130.1 10.1785/0220180306 10.1007/978-3-540-44999-7_4 10.21273/HORTSCI.43.6.1643 10.1175/WAF896.1 |
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| Keywords | frost damage random forests temperature prediction machine learning neural networks |
| Language | English |
| License | Copyright © 2023 Talsma, Solander, Mudunuru, Crawford and Powell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| SubjectTerms | Artificial Intelligence earth sciences frost damage frost damage, machine learning, neural network, random forests, temperature prediction, weather models machine learning MATHEMATICS AND COMPUTING neural networks random forests temperature prediction |
| Title | Frost prediction using machine learning and deep neural network models |
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