Crop Yield Prediction in Precision Agriculture
Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/10/2460 |
| _version_ | 1827652293321818112 |
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| author | Anikó Nyéki Miklós Neményi |
| author_facet | Anikó Nyéki Miklós Neményi |
| author_sort | Anikó Nyéki |
| collection | DOAJ |
| description | Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species. |
| first_indexed | 2024-03-09T20:53:28Z |
| format | Article |
| id | doaj.art-55a4ac433df844e2b0ccdd7fe7388034 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:53:28Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-55a4ac433df844e2b0ccdd7fe73880342023-11-23T22:27:34ZengMDPI AGAgronomy2073-43952022-10-011210246010.3390/agronomy12102460Crop Yield Prediction in Precision AgricultureAnikó Nyéki0Miklós Neményi1Department of Biosystems Engineering and Precision Technology, Széchenyi István University, 9200 Mosonmagyaróvár, HungaryDepartment of Biosystems Engineering and Precision Technology, Széchenyi István University, 9200 Mosonmagyaróvár, HungaryPredicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.https://www.mdpi.com/2073-4395/12/10/2460crop modelsartificial intelligencebig dataIoTyield influencing variablesyield forecasting |
| spellingShingle | Anikó Nyéki Miklós Neményi Crop Yield Prediction in Precision Agriculture Agronomy crop models artificial intelligence big data IoT yield influencing variables yield forecasting |
| title | Crop Yield Prediction in Precision Agriculture |
| title_full | Crop Yield Prediction in Precision Agriculture |
| title_fullStr | Crop Yield Prediction in Precision Agriculture |
| title_full_unstemmed | Crop Yield Prediction in Precision Agriculture |
| title_short | Crop Yield Prediction in Precision Agriculture |
| title_sort | crop yield prediction in precision agriculture |
| topic | crop models artificial intelligence big data IoT yield influencing variables yield forecasting |
| url | https://www.mdpi.com/2073-4395/12/10/2460 |
| work_keys_str_mv | AT anikonyeki cropyieldpredictioninprecisionagriculture AT miklosnemenyi cropyieldpredictioninprecisionagriculture |