Methodology for multi-temporal prediction of crop rotations using recurrent neural networks
In a context of growing demand for food and the scarcity of natural resources, the development of more sustainable agriculture is imperative. This means it is necessary to limit the environmental impact of agricultural activities on soil and water and to be mindful of the carbon footprint, while mai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375522001162 |
| _version_ | 1797838529922859008 |
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| author | Ambre Dupuis Camélia Dadouchi Bruno Agard |
| author_facet | Ambre Dupuis Camélia Dadouchi Bruno Agard |
| author_sort | Ambre Dupuis |
| collection | DOAJ |
| description | In a context of growing demand for food and the scarcity of natural resources, the development of more sustainable agriculture is imperative. This means it is necessary to limit the environmental impact of agricultural activities on soil and water and to be mindful of the carbon footprint, while maintaining crop yields and economic benefits for producers. Crop rotation is a valuable tool in sustainable agriculture, but this technique has to be appropriately coupled with sustainable fertilization plans to optimize crops. The proposed methodology uses recurrent neural networks (RNN); more precisely, LSTMs, in a Seq2Seq architecture, to predict the most probable scenarios of crop rotations to be exploited in a field in subsequent growing seasons, according to cropping habits. The output can be used in crop models to build a decision support system for greater sustainability in agricultural production by allowing producers to choose the strategy that offers the best compromise between profitability and environmental impact. |
| first_indexed | 2024-04-09T15:43:24Z |
| format | Article |
| id | doaj.art-b4e014a405d3457785ad42656f03f378 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-09T15:43:24Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-b4e014a405d3457785ad42656f03f3782023-04-27T06:08:20ZengElsevierSmart Agricultural Technology2772-37552023-08-014100152Methodology for multi-temporal prediction of crop rotations using recurrent neural networksAmbre Dupuis0Camélia Dadouchi1Bruno Agard2Corresponding author.; Laboratoire en Intelligence des Données (LID), Canada; Centre Interuniversitaire de Recherche sur les Réseaux dEntreprise, la Logistique et le Transport (CIRRELT), Canada; Département de mathématiques et génie industriel Polytechnique Montréal, CP 6079, succ. Centre-Ville, Montréal, Québec, CanadaLaboratoire en Intelligence des Données (LID), Canada; Centre Interuniversitaire de Recherche sur les Réseaux dEntreprise, la Logistique et le Transport (CIRRELT), Canada; Département de mathématiques et génie industriel Polytechnique Montréal, CP 6079, succ. Centre-Ville, Montréal, Québec, CanadaLaboratoire en Intelligence des Données (LID), Canada; Centre Interuniversitaire de Recherche sur les Réseaux dEntreprise, la Logistique et le Transport (CIRRELT), Canada; Département de mathématiques et génie industriel Polytechnique Montréal, CP 6079, succ. Centre-Ville, Montréal, Québec, CanadaIn a context of growing demand for food and the scarcity of natural resources, the development of more sustainable agriculture is imperative. This means it is necessary to limit the environmental impact of agricultural activities on soil and water and to be mindful of the carbon footprint, while maintaining crop yields and economic benefits for producers. Crop rotation is a valuable tool in sustainable agriculture, but this technique has to be appropriately coupled with sustainable fertilization plans to optimize crops. The proposed methodology uses recurrent neural networks (RNN); more precisely, LSTMs, in a Seq2Seq architecture, to predict the most probable scenarios of crop rotations to be exploited in a field in subsequent growing seasons, according to cropping habits. The output can be used in crop models to build a decision support system for greater sustainability in agricultural production by allowing producers to choose the strategy that offers the best compromise between profitability and environmental impact.http://www.sciencedirect.com/science/article/pii/S2772375522001162Agriculture 4.0Crop rotationDeep learningSeq2SeqLSTM |
| spellingShingle | Ambre Dupuis Camélia Dadouchi Bruno Agard Methodology for multi-temporal prediction of crop rotations using recurrent neural networks Smart Agricultural Technology Agriculture 4.0 Crop rotation Deep learning Seq2Seq LSTM |
| title | Methodology for multi-temporal prediction of crop rotations using recurrent neural networks |
| title_full | Methodology for multi-temporal prediction of crop rotations using recurrent neural networks |
| title_fullStr | Methodology for multi-temporal prediction of crop rotations using recurrent neural networks |
| title_full_unstemmed | Methodology for multi-temporal prediction of crop rotations using recurrent neural networks |
| title_short | Methodology for multi-temporal prediction of crop rotations using recurrent neural networks |
| title_sort | methodology for multi temporal prediction of crop rotations using recurrent neural networks |
| topic | Agriculture 4.0 Crop rotation Deep learning Seq2Seq LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2772375522001162 |
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