Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields
Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/14/2230 |
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| author | Angela Kross Evelyn Znoj Daihany Callegari Gurpreet Kaur Mark Sunohara David R. Lapen Heather McNairn |
| author_facet | Angela Kross Evelyn Znoj Daihany Callegari Gurpreet Kaur Mark Sunohara David R. Lapen Heather McNairn |
| author_sort | Angela Kross |
| collection | DOAJ |
| description | Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this study, an artificial neural network (ANN) method was used: (1) to evaluate the relative importance of predictor variables for the prediction of within-field corn and soybean end-of-season yield and (2) to evaluate the performance of the ANN models with a minimal optimized variable dataset for their capacity to predict corn and soybean yield over multiple years at the within-field level. Several satellite derived vegetation indices (normalized difference vegetation index—NDVI, red edge NDVI and simple ratio—SR) and elevation derived variables (slope, flow accumulation, aspect) were used as crop yield predictor variables, hypothesizing that the different variables reflect different crop and site conditions. The study identified the SR index and the slope as the most important predictor variables for both crop types during two training and testing years (2011, 2012). The dates of the most important SR images, however, were different for the two crop types and corresponded to their critical crop developmental stages (phenology). The relative mean absolute errors were overall smaller for corn compared to soybean: all of the 2011 corn study fields had errors below 10%; 75% of the fields had errors below 10% in 2012. The errors were more variable for soybean. In 2011, 37% of the fields had errors below 10%, while in 2012, 100% of the fields had errors below 20%. The results are promising and can provide yield estimates at the farm level, which could be useful in refining broader scale (e.g., county, region) yield projections. |
| first_indexed | 2024-03-10T18:31:34Z |
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| id | doaj.art-73ff1fdc164e4a0295907a2c13c4ba87 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:31:34Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-73ff1fdc164e4a0295907a2c13c4ba872023-11-20T06:32:27ZengMDPI AGRemote Sensing2072-42922020-07-011214223010.3390/rs12142230Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean YieldsAngela Kross0Evelyn Znoj1Daihany Callegari2Gurpreet Kaur3Mark Sunohara4David R. Lapen5Heather McNairn6Department of Geography, Planning and Environment, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Geography, Planning and Environment, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Agronomy, Federal Rural University of Amazon, Paragominas, PA 68627-451, BrazilDepartment of Geography, Planning and Environment, Concordia University, Montreal, QC H3G 1M8, CanadaOttawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, CanadaOttawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, CanadaOttawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, CanadaCrop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this study, an artificial neural network (ANN) method was used: (1) to evaluate the relative importance of predictor variables for the prediction of within-field corn and soybean end-of-season yield and (2) to evaluate the performance of the ANN models with a minimal optimized variable dataset for their capacity to predict corn and soybean yield over multiple years at the within-field level. Several satellite derived vegetation indices (normalized difference vegetation index—NDVI, red edge NDVI and simple ratio—SR) and elevation derived variables (slope, flow accumulation, aspect) were used as crop yield predictor variables, hypothesizing that the different variables reflect different crop and site conditions. The study identified the SR index and the slope as the most important predictor variables for both crop types during two training and testing years (2011, 2012). The dates of the most important SR images, however, were different for the two crop types and corresponded to their critical crop developmental stages (phenology). The relative mean absolute errors were overall smaller for corn compared to soybean: all of the 2011 corn study fields had errors below 10%; 75% of the fields had errors below 10% in 2012. The errors were more variable for soybean. In 2011, 37% of the fields had errors below 10%, while in 2012, 100% of the fields had errors below 20%. The results are promising and can provide yield estimates at the farm level, which could be useful in refining broader scale (e.g., county, region) yield projections.https://www.mdpi.com/2072-4292/12/14/2230cornsoybeanyield predictionremote sensingvegetation indicesartificial neural network |
| spellingShingle | Angela Kross Evelyn Znoj Daihany Callegari Gurpreet Kaur Mark Sunohara David R. Lapen Heather McNairn Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields Remote Sensing corn soybean yield prediction remote sensing vegetation indices artificial neural network |
| title | Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields |
| title_full | Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields |
| title_fullStr | Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields |
| title_full_unstemmed | Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields |
| title_short | Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields |
| title_sort | using artificial neural networks and remotely sensed data to evaluate the relative importance of variables for prediction of within field corn and soybean yields |
| topic | corn soybean yield prediction remote sensing vegetation indices artificial neural network |
| url | https://www.mdpi.com/2072-4292/12/14/2230 |
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