Machine learning for supporting irrigation decisions based on climatic water balance
A machine learning model was developed to support irrigation decisions. The field research was conducted on ‘Gala’ apple trees. For each week during the growing seasons (2009–2013), the following parameters were determined: precipitation, evapotranspiration (Penman–Monteith formula), crop (apple) ev...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2023-09-01
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| Series: | Journal of Water and Land Development |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/128399/PDF/2023-03-JWLD-04.pdf |
| _version_ | 1797659221126283264 |
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| author | Waldemar Treder Krzysztof Klamkowski Katarzyna Wójcik Anna Tryngiel-Gać |
| author_facet | Waldemar Treder Krzysztof Klamkowski Katarzyna Wójcik Anna Tryngiel-Gać |
| author_sort | Waldemar Treder |
| collection | DOAJ |
| description | A machine learning model was developed to support irrigation decisions. The field research was conducted on ‘Gala’ apple trees. For each week during the growing seasons (2009–2013), the following parameters were determined: precipitation, evapotranspiration (Penman–Monteith formula), crop (apple) evapotranspiration, climatic water balance, crop (apple) water balance (AWB), cumulative climatic water balance (determined weekly, ΣCWB), cumulative apple water balance (ΣAWB), week number from full bloom, and nominal classification variable: irrigation, no irrigation. Statistical analyses were performed with the use of the WEKA 3.9 application software. The attribute evaluator was performed using Correlation Attribute Eval with the Ranker Search Method. Due to its highest accuracy, the final analyses were performed using the WEKA classifier package with the J48graft algorithm. For each of the analysed growing seasons, different correlations were found between the water balance determined for apple trees and the actual water balance of the soil layer (10–30 cm). The model made correct decisions in 76.7% of the instances when watering was needed and in 87.7% of the instances when watering was not needed. The root of the classification tree was the AWB determined for individual weeks of the growing season. The high places in the tree hierarchy were occupied by the nodes defining the elapsed time of the growing season, the values of ΣCWB and ΣAWB. |
| first_indexed | 2024-03-11T18:11:44Z |
| format | Article |
| id | doaj.art-02118a88d88d40b8982eaddede626c3f |
| institution | Directory Open Access Journal |
| issn | 2083-4535 |
| language | English |
| last_indexed | 2024-03-11T18:11:44Z |
| publishDate | 2023-09-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Journal of Water and Land Development |
| spelling | doaj.art-02118a88d88d40b8982eaddede626c3f2023-10-16T12:12:37ZengPolish Academy of SciencesJournal of Water and Land Development2083-45352023-09-01No 582530https://doi.org/10.24425/jwld.2023.145358Machine learning for supporting irrigation decisions based on climatic water balanceWaldemar Treder0https://orcid.org/0000-0003-1640-9671Krzysztof Klamkowski1https://orcid.org/0000-0003-0358-3726Katarzyna Wójcik2https://orcid.org/0000-0001-7802-9733Anna Tryngiel-Gać3https://orcid.org/0000-0002-8766-6010National Institute of Horticultural Research, Konstytucji 3 Maja St, 1/3, 96-100 Skierniewice, PolandNational Institute of Horticultural Research, Konstytucji 3 Maja St, 1/3, 96-100 Skierniewice, PolandNational Institute of Horticultural Research, Konstytucji 3 Maja St, 1/3, 96-100 Skierniewice, PolandNational Institute of Horticultural Research, Konstytucji 3 Maja St, 1/3, 96-100 Skierniewice, PolandA machine learning model was developed to support irrigation decisions. The field research was conducted on ‘Gala’ apple trees. For each week during the growing seasons (2009–2013), the following parameters were determined: precipitation, evapotranspiration (Penman–Monteith formula), crop (apple) evapotranspiration, climatic water balance, crop (apple) water balance (AWB), cumulative climatic water balance (determined weekly, ΣCWB), cumulative apple water balance (ΣAWB), week number from full bloom, and nominal classification variable: irrigation, no irrigation. Statistical analyses were performed with the use of the WEKA 3.9 application software. The attribute evaluator was performed using Correlation Attribute Eval with the Ranker Search Method. Due to its highest accuracy, the final analyses were performed using the WEKA classifier package with the J48graft algorithm. For each of the analysed growing seasons, different correlations were found between the water balance determined for apple trees and the actual water balance of the soil layer (10–30 cm). The model made correct decisions in 76.7% of the instances when watering was needed and in 87.7% of the instances when watering was not needed. The root of the classification tree was the AWB determined for individual weeks of the growing season. The high places in the tree hierarchy were occupied by the nodes defining the elapsed time of the growing season, the values of ΣCWB and ΣAWB.https://journals.pan.pl/Content/128399/PDF/2023-03-JWLD-04.pdfapple treesevapotranspirationirrigation schedulingmachine learningprecipitationweka software |
| spellingShingle | Waldemar Treder Krzysztof Klamkowski Katarzyna Wójcik Anna Tryngiel-Gać Machine learning for supporting irrigation decisions based on climatic water balance Journal of Water and Land Development apple trees evapotranspiration irrigation scheduling machine learning precipitation weka software |
| title | Machine learning for supporting irrigation decisions based on climatic water balance |
| title_full | Machine learning for supporting irrigation decisions based on climatic water balance |
| title_fullStr | Machine learning for supporting irrigation decisions based on climatic water balance |
| title_full_unstemmed | Machine learning for supporting irrigation decisions based on climatic water balance |
| title_short | Machine learning for supporting irrigation decisions based on climatic water balance |
| title_sort | machine learning for supporting irrigation decisions based on climatic water balance |
| topic | apple trees evapotranspiration irrigation scheduling machine learning precipitation weka software |
| url | https://journals.pan.pl/Content/128399/PDF/2023-03-JWLD-04.pdf |
| work_keys_str_mv | AT waldemartreder machinelearningforsupportingirrigationdecisionsbasedonclimaticwaterbalance AT krzysztofklamkowski machinelearningforsupportingirrigationdecisionsbasedonclimaticwaterbalance AT katarzynawojcik machinelearningforsupportingirrigationdecisionsbasedonclimaticwaterbalance AT annatryngielgac machinelearningforsupportingirrigationdecisionsbasedonclimaticwaterbalance |