Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging
Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and disea...
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MDPI AG
2024-10-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/6/4/208 |
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| author | Asparuh I. Atanasov Hristo P. Stoyanov Atanas Z. Atanasov |
| author_facet | Asparuh I. Atanasov Hristo P. Stoyanov Atanas Z. Atanasov |
| author_sort | Asparuh I. Atanasov |
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| description | Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and diseases in the investigated fields. This study aims to assess the potential for differentiating growth patterns in winter wheat cultivars by examining them with two unmanned aerial vehicles (UAVs), the Mavic 2 Pro and Phantom 4 Pro, equipped with a multispectral camera from the MAPIR™ brand. Based on an experimental study conducted in the Southern Dobruja region (Bulgaria), vegetation reflectance indices, such as the Normalized-Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index 2 (EVI2), were generated, and a database was created to track their changing trends. The obtained results showed that the values of the NDVI, EVI2, and SAVI can be used to predict the productive potential of wheat, but only after accounting for the meteorological conditions of the respective growing season. The proposed methodology provides accurate results in small areas, with a resolution of 0.40 cm/pixel when flying at an altitude of 12 m and 2.3 cm/pixel when flying at an altitude of 100 m. The achieved precision in small and ultra-small agricultural areas, at a width of 1.2 m, will help wheat breeders conduct precise diagnostics of individual wheat varieties. |
| format | Article |
| id | doaj-art-b1792bceaab24551b66e98eb85fc5dd4 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | AgriEngineering |
| spelling | doaj-art-b1792bceaab24551b66e98eb85fc5dd42024-12-27T14:03:32ZengMDPI AGAgriEngineering2624-74022024-10-01643652367110.3390/agriengineering6040208Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle ImagingAsparuh I. Atanasov0Hristo P. Stoyanov1Atanas Z. Atanasov2Department of Mechanics and Elements of Machines, Technical University of Varna, 9010 Varna, BulgariaDobrudzha Agricultural Institute General Toshevo, 9521 Petleshkovo, BulgariaDepartment of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, BulgariaWheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and diseases in the investigated fields. This study aims to assess the potential for differentiating growth patterns in winter wheat cultivars by examining them with two unmanned aerial vehicles (UAVs), the Mavic 2 Pro and Phantom 4 Pro, equipped with a multispectral camera from the MAPIR™ brand. Based on an experimental study conducted in the Southern Dobruja region (Bulgaria), vegetation reflectance indices, such as the Normalized-Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index 2 (EVI2), were generated, and a database was created to track their changing trends. The obtained results showed that the values of the NDVI, EVI2, and SAVI can be used to predict the productive potential of wheat, but only after accounting for the meteorological conditions of the respective growing season. The proposed methodology provides accurate results in small areas, with a resolution of 0.40 cm/pixel when flying at an altitude of 12 m and 2.3 cm/pixel when flying at an altitude of 100 m. The achieved precision in small and ultra-small agricultural areas, at a width of 1.2 m, will help wheat breeders conduct precise diagnostics of individual wheat varieties.https://www.mdpi.com/2624-7402/6/4/208agricultural monitoringdronesinfrared imagingprecision agriculturevegetation indices |
| spellingShingle | Asparuh I. Atanasov Hristo P. Stoyanov Atanas Z. Atanasov Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging AgriEngineering agricultural monitoring drones infrared imaging precision agriculture vegetation indices |
| title | Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging |
| title_full | Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging |
| title_fullStr | Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging |
| title_full_unstemmed | Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging |
| title_short | Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging |
| title_sort | differentiating growth patterns in winter wheat cultivars via unmanned aerial vehicle imaging |
| topic | agricultural monitoring drones infrared imaging precision agriculture vegetation indices |
| url | https://www.mdpi.com/2624-7402/6/4/208 |
| work_keys_str_mv | AT asparuhiatanasov differentiatinggrowthpatternsinwinterwheatcultivarsviaunmannedaerialvehicleimaging AT hristopstoyanov differentiatinggrowthpatternsinwinterwheatcultivarsviaunmannedaerialvehicleimaging AT atanaszatanasov differentiatinggrowthpatternsinwinterwheatcultivarsviaunmannedaerialvehicleimaging |