Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging
Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/12/3152 |
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| author | Jiří Janoušek Petr Marcoň Přemysl Dohnal Václav Jambor Hana Synková Petr Raichl |
| author_facet | Jiří Janoušek Petr Marcoň Přemysl Dohnal Václav Jambor Hana Synková Petr Raichl |
| author_sort | Jiří Janoušek |
| collection | DOAJ |
| description | Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (<i>NDRE</i>), normalized difference vegetation index (<i>NDVI</i>), green normalized difference vegetation index (<i>GNDVI</i>), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic. |
| first_indexed | 2024-03-11T01:58:31Z |
| format | Article |
| id | doaj.art-fba9124c598a4d0ba65dd33b73701bf1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:58:31Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fba9124c598a4d0ba65dd33b73701bf12023-11-18T12:27:12ZengMDPI AGRemote Sensing2072-42922023-06-011512315210.3390/rs15123152Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field ImagingJiří Janoušek0Petr Marcoň1Přemysl Dohnal2Václav Jambor3Hana Synková4Petr Raichl5Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech RepublicFaculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech RepublicFaculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech RepublicNutriVet s.r.o., Vídeňská 1023, 69123 Pohořelice, Czech RepublicNutriVet s.r.o., Vídeňská 1023, 69123 Pohořelice, Czech RepublicFaculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech RepublicEstimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (<i>NDRE</i>), normalized difference vegetation index (<i>NDVI</i>), green normalized difference vegetation index (<i>GNDVI</i>), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.https://www.mdpi.com/2072-4292/15/12/3152cornmultispectral imagingvegetation indicesnutritional analysiscorrelationphotogrammetry |
| spellingShingle | Jiří Janoušek Petr Marcoň Přemysl Dohnal Václav Jambor Hana Synková Petr Raichl Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging Remote Sensing corn multispectral imaging vegetation indices nutritional analysis correlation photogrammetry |
| title | Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging |
| title_full | Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging |
| title_fullStr | Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging |
| title_full_unstemmed | Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging |
| title_short | Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging |
| title_sort | predicting the optimum corn harvest time via the quantity of dry matter determined with vegetation indices obtained from multispectral field imaging |
| topic | corn multispectral imaging vegetation indices nutritional analysis correlation photogrammetry |
| url | https://www.mdpi.com/2072-4292/15/12/3152 |
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