Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks
The objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted featu...
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MDPI AG
2022-06-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/14/11/2692 |
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author | Kirsi Karila Raquel Alves Oliveira Johannes Ek Jere Kaivosoja Niko Koivumäki Panu Korhonen Oiva Niemeläinen Laura Nyholm Roope Näsi Ilkka Pölönen Eija Honkavaara |
author_facet | Kirsi Karila Raquel Alves Oliveira Johannes Ek Jere Kaivosoja Niko Koivumäki Panu Korhonen Oiva Niemeläinen Laura Nyholm Roope Näsi Ilkka Pölönen Eija Honkavaara |
author_sort | Kirsi Karila |
collection | DOAJ |
description | The objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted features. The parameters included fresh and dry biomass (FY, DMY), the digestibility of organic matter in dry matter (D-value), neutral detergent fiber (NDF), indigestible neutral detergent fiber (iNDF), water-soluble carbohydrates (WSC), nitrogen concentration (Ncont) and nitrogen uptake (NU); datasets from spring and summer growth were used. Deep pre-trained neural network architectures, the VGG16 and the Vision Transformer (ViT), and simple 2D and 3D convolutional neural networks (CNN) were studied. In most cases, the neural networks outperformed RF. The normalized root-mean-square errors (NRMSE) of the best models were for FY 19% (2104 kg/ha), DMY 21% (512 kg DM/ha), D-value 1.2% (8.6 g/kg DM), iNDF 12% (5.1 g/kg DM), NDF 1.1% (6.2 g/kg DM), WSC 10% (10.5 g/kg DM), Ncont 9% (2 g N/kg DM), and NU 22% (11.9 N kg/ha) using independent test dataset. The RGB data provided good results, particularly for the FY, DMY, WSC and NU. The HSI datasets provided advantages for some parameters. The ViT and VGG provided the best results with the RGB data, whereas the simple 3D-CNN was the most consistent with the HSI data. |
first_indexed | 2024-03-10T00:54:29Z |
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institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T00:54:29Z |
publishDate | 2022-06-01 |
publisher | MDPI AG |
record_format | Article |
series | Remote Sensing |
spelling | doaj.art-18e58c6e52b444fc9aafa571ece017022023-11-23T14:45:53ZengMDPI AGRemote Sensing2072-42922022-06-011411269210.3390/rs14112692Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural NetworksKirsi Karila0Raquel Alves Oliveira1Johannes Ek2Jere Kaivosoja3Niko Koivumäki4Panu Korhonen5Oiva Niemeläinen6Laura Nyholm7Roope Näsi8Ilkka Pölönen9Eija Honkavaara10Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, FinlandFinnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, FinlandDepartment of Applied Physics, School of Science, Aalto University, 02150 Espoo, FinlandNatural Resources Institute Finland (Luke), 00790 Helsinki, FinlandFinnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, FinlandNatural Resources Institute Finland (Luke), 00790 Helsinki, FinlandNatural Resources Institute Finland (Luke), 00790 Helsinki, FinlandFarm Services, Valio Ltd., 00370 Helsinki, FinlandFinnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, FinlandFaculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandFinnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, FinlandThe objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted features. The parameters included fresh and dry biomass (FY, DMY), the digestibility of organic matter in dry matter (D-value), neutral detergent fiber (NDF), indigestible neutral detergent fiber (iNDF), water-soluble carbohydrates (WSC), nitrogen concentration (Ncont) and nitrogen uptake (NU); datasets from spring and summer growth were used. Deep pre-trained neural network architectures, the VGG16 and the Vision Transformer (ViT), and simple 2D and 3D convolutional neural networks (CNN) were studied. In most cases, the neural networks outperformed RF. The normalized root-mean-square errors (NRMSE) of the best models were for FY 19% (2104 kg/ha), DMY 21% (512 kg DM/ha), D-value 1.2% (8.6 g/kg DM), iNDF 12% (5.1 g/kg DM), NDF 1.1% (6.2 g/kg DM), WSC 10% (10.5 g/kg DM), Ncont 9% (2 g N/kg DM), and NU 22% (11.9 N kg/ha) using independent test dataset. The RGB data provided good results, particularly for the FY, DMY, WSC and NU. The HSI datasets provided advantages for some parameters. The ViT and VGG provided the best results with the RGB data, whereas the simple 3D-CNN was the most consistent with the HSI data.https://www.mdpi.com/2072-4292/14/11/2692droneremote sensinghyperspectralRGBCNNimage transformer |
spellingShingle | Kirsi Karila Raquel Alves Oliveira Johannes Ek Jere Kaivosoja Niko Koivumäki Panu Korhonen Oiva Niemeläinen Laura Nyholm Roope Näsi Ilkka Pölönen Eija Honkavaara Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks Remote Sensing drone remote sensing hyperspectral RGB CNN image transformer |
title | Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks |
title_full | Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks |
title_fullStr | Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks |
title_full_unstemmed | Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks |
title_short | Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks |
title_sort | estimating grass sward quality and quantity parameters using drone remote sensing with deep neural networks |
topic | drone remote sensing hyperspectral RGB CNN image transformer |
url | https://www.mdpi.com/2072-4292/14/11/2692 |
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