Comparison of the machine learning and AquaCrop models for quinoa crops
One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For th...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2023-05-01
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| Series: | Research in Agricultural Engineering |
| Subjects: | |
| Online Access: | https://rae.agriculturejournals.cz/artkey/rae-202302-0002_comparison-of-the-machine-learning-and-aquacrop-models-for-quinoa-crops.php |
| _version_ | 1797338166050422784 |
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| author | Rossy Chumbe Stefany Silva Yvan Garcia |
| author_facet | Rossy Chumbe Stefany Silva Yvan Garcia |
| author_sort | Rossy Chumbe |
| collection | DOAJ |
| description | One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best P-value indicator with a value of 0.962 and a smaller margin of error in relation to the mean absolute error (MAE) indicator with a value of 0.629. Likewise, it was identified that, for the simulation period of 190 days, 472.35 mm of water was required to carry out the irrigation process in red quinoa crops. |
| first_indexed | 2024-03-08T09:27:04Z |
| format | Article |
| id | doaj.art-8f240d8da8e1487193d801f31fdd9f09 |
| institution | Directory Open Access Journal |
| issn | 1212-9151 1805-9376 |
| language | English |
| last_indexed | 2024-03-08T09:27:04Z |
| publishDate | 2023-05-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Research in Agricultural Engineering |
| spelling | doaj.art-8f240d8da8e1487193d801f31fdd9f092024-01-31T08:07:42ZengCzech Academy of Agricultural SciencesResearch in Agricultural Engineering1212-91511805-93762023-05-01692657510.17221/86/2021-RAErae-202302-0002Comparison of the machine learning and AquaCrop models for quinoa cropsRossy Chumbe0Stefany Silva1Yvan Garcia2Department Industrial Engineering, Faculty of Engineering, University of Lima, Lima, PeruDepartment Industrial Engineering, Faculty of Engineering, University of Lima, Lima, PeruDepartment Industrial Engineering, Faculty of Engineering, University of Lima, Lima, PeruOne of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best P-value indicator with a value of 0.962 and a smaller margin of error in relation to the mean absolute error (MAE) indicator with a value of 0.629. Likewise, it was identified that, for the simulation period of 190 days, 472.35 mm of water was required to carry out the irrigation process in red quinoa crops.https://rae.agriculturejournals.cz/artkey/rae-202302-0002_comparison-of-the-machine-learning-and-aquacrop-models-for-quinoa-crops.phpadaboostirrigation systempredictive analysisstatistical analysiswater management |
| spellingShingle | Rossy Chumbe Stefany Silva Yvan Garcia Comparison of the machine learning and AquaCrop models for quinoa crops Research in Agricultural Engineering adaboost irrigation system predictive analysis statistical analysis water management |
| title | Comparison of the machine learning and AquaCrop models for quinoa crops |
| title_full | Comparison of the machine learning and AquaCrop models for quinoa crops |
| title_fullStr | Comparison of the machine learning and AquaCrop models for quinoa crops |
| title_full_unstemmed | Comparison of the machine learning and AquaCrop models for quinoa crops |
| title_short | Comparison of the machine learning and AquaCrop models for quinoa crops |
| title_sort | comparison of the machine learning and aquacrop models for quinoa crops |
| topic | adaboost irrigation system predictive analysis statistical analysis water management |
| url | https://rae.agriculturejournals.cz/artkey/rae-202302-0002_comparison-of-the-machine-learning-and-aquacrop-models-for-quinoa-crops.php |
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