Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach
dc.creator | Bebie M., Cavalaris C., Kyparissis A. | en |
dc.date.accessioned | 2023-01-31T07:37:06Z | |
dc.date.available | 2023-01-31T07:37:06Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.3390/rs14163880 | |
dc.identifier.issn | 20724292 | |
dc.identifier.uri | http://hdl.handle.net/11615/71276 | |
dc.description.abstract | Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression model (VI-MLR) based on vegetation indices (EVI, NMDI) was used. In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods were used as input parameters in three machine learning model algorithms, i.e., random forest (RF), k-nearest neighbors (KNN), and boosting regressions (BR). Modeling results were examined against yield data collected by a combine harvester equipped with a yield mapping system. VI-MLR showed a moderate performance with R2 = 0.532 and RMSE = 847 kg ha−1. All machine learning approaches enhanced model accuracy when all images during the growing periods were used, especially RF and KNN (R2 > 0.91, RMSE < 360 kg ha−1). Additionally, RF and KNN accuracy remained high (R2 > 0.87, RMSE < 455 kg ha−1) when images from the start of the growing period until March, i.e., three months before harvest, were used, indicating the high suitability of machine learning on Sentinel-2 data for early yield prediction of durum wheat, information considered essential for precision agriculture applications. © 2022 by the authors. | en |
dc.language.iso | en | en |
dc.source | Remote Sensing | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137804965&doi=10.3390%2frs14163880&partnerID=40&md5=7308411dd3de8482f0c739046128483d | |
dc.subject | Adaptive boosting | en |
dc.subject | Decision trees | en |
dc.subject | Image enhancement | en |
dc.subject | Linear regression | en |
dc.subject | Nearest neighbor search | en |
dc.subject | Random forests | en |
dc.subject | Vegetation | en |
dc.subject | Durum wheats | en |
dc.subject | Growing period | en |
dc.subject | Machine learning approaches | en |
dc.subject | Machine-learning | en |
dc.subject | Modeling approach | en |
dc.subject | Random forests | en |
dc.subject | Sentinel-2 | en |
dc.subject | Vegetation index | en |
dc.subject | Wheat yield | en |
dc.subject | Yield models | en |
dc.subject | Machine learning | en |
dc.subject | MDPI | en |
dc.title | Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach | en |
dc.type | journalArticle | en |
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