Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR
This article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/7/1734 |
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| author | Gian Oré Marlon S. Alcântara Juliana A. Góes Bárbara Teruel Luciano P. Oliveira Jhonnatan Yepes Valquíria Castro Leonardo S. Bins Felicio Castro Dieter Luebeck Laila F. Moreira Rodrigo Cintra Lucas H. Gabrielli Hugo E. Hernandez-Figueroa |
| author_facet | Gian Oré Marlon S. Alcântara Juliana A. Góes Bárbara Teruel Luciano P. Oliveira Jhonnatan Yepes Valquíria Castro Leonardo S. Bins Felicio Castro Dieter Luebeck Laila F. Moreira Rodrigo Cintra Lucas H. Gabrielli Hugo E. Hernandez-Figueroa |
| author_sort | Gian Oré |
| collection | DOAJ |
| description | This article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of 2 kg m<sup>−2</sup> in sugarcane crops, which is an unprecedented result compared with other works based on the Synthetic Aperture Radar (SAR) system. By correlating the field measurements of the ripening index (RI) with the AGB measurements by radar, an indirect estimate of the RI by the radar is obtained. It is observed that the AGB reaches its maximum approximately 280 days after planting and the maximum RI, which defines the harvesting date, approximately 360 days after planting for the species IACSP97-4039. Starting from an AGB map collected by the radar, it is then possible to predict the harvesting date and the corresponding productivity with competitive average errors of 8 days and 10.7%, respectively, with three months in advance, whereas typical methods employed on a test site achieve an average error of 30 days with three months in advance. To the best of our knowledge, it is the first time that a multi-band radar is employed for productivity prediction in sugarcane crops. |
| first_indexed | 2024-03-09T11:27:16Z |
| format | Article |
| id | doaj.art-28b8b79ecff94083800eb6cb61742b8a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:27:16Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-28b8b79ecff94083800eb6cb61742b8a2023-11-30T23:58:15ZengMDPI AGRemote Sensing2072-42922022-04-01147173410.3390/rs14071734Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SARGian Oré0Marlon S. Alcântara1Juliana A. Góes2Bárbara Teruel3Luciano P. Oliveira4Jhonnatan Yepes5Valquíria Castro6Leonardo S. Bins7Felicio Castro8Dieter Luebeck9Laila F. Moreira10Rodrigo Cintra11Lucas H. Gabrielli12Hugo E. Hernandez-Figueroa13School of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilSchool of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, BrazilDirected Energy Research Centre, Technology Innovation Institute, Abu Dhabi P.O. Box 9639, United Arab EmiratesSchool of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilNational Institute for Space Research—INPE, São José dos Campos 12227-010, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilRadaz Indústria e Comércio de Produtos Eletrônicos Ltda., São José dos Campos 12244-000, BrazilRadaz Indústria e Comércio de Produtos Eletrônicos Ltda., São José dos Campos 12244-000, BrazilSão Martinho SA, Sao Paulo 14850-000, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilThis article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of 2 kg m<sup>−2</sup> in sugarcane crops, which is an unprecedented result compared with other works based on the Synthetic Aperture Radar (SAR) system. By correlating the field measurements of the ripening index (RI) with the AGB measurements by radar, an indirect estimate of the RI by the radar is obtained. It is observed that the AGB reaches its maximum approximately 280 days after planting and the maximum RI, which defines the harvesting date, approximately 360 days after planting for the species IACSP97-4039. Starting from an AGB map collected by the radar, it is then possible to predict the harvesting date and the corresponding productivity with competitive average errors of 8 days and 10.7%, respectively, with three months in advance, whereas typical methods employed on a test site achieve an average error of 30 days with three months in advance. To the best of our knowledge, it is the first time that a multi-band radar is employed for productivity prediction in sugarcane crops.https://www.mdpi.com/2072-4292/14/7/1734sugarcane biomass estimationharvest predictiondrone-borne SARback-projection processor |
| spellingShingle | Gian Oré Marlon S. Alcântara Juliana A. Góes Bárbara Teruel Luciano P. Oliveira Jhonnatan Yepes Valquíria Castro Leonardo S. Bins Felicio Castro Dieter Luebeck Laila F. Moreira Rodrigo Cintra Lucas H. Gabrielli Hugo E. Hernandez-Figueroa Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR Remote Sensing sugarcane biomass estimation harvest prediction drone-borne SAR back-projection processor |
| title | Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR |
| title_full | Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR |
| title_fullStr | Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR |
| title_full_unstemmed | Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR |
| title_short | Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR |
| title_sort | predicting sugarcane harvest date and productivity with a drone borne tri band sar |
| topic | sugarcane biomass estimation harvest prediction drone-borne SAR back-projection processor |
| url | https://www.mdpi.com/2072-4292/14/7/1734 |
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