A1 Journal article (refereed)
Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks (2022)


Karila, K., Alves Oliveira, R., Ek, J., Kaivosoja, J., Koivumäki, N., Korhonen, P., Niemeläinen, O., Nyholm, L., Näsi, R., Pölönen, I., & Honkavaara, E. (2022). Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks. Remote Sensing, 14(11), Article 2692. https://doi.org/10.3390/rs14112692


JYU authors or editors


Publication details

All authors or editorsKarila, Kirsi; Alves Oliveira, Raquel; Ek, Johannes; Kaivosoja, Jere; Koivumäki, Niko; Korhonen, Panu; Niemeläinen, Oiva; Nyholm, Laura; Näsi, Roope; Pölönen, Ilkka; et al.

Journal or seriesRemote Sensing

eISSN2072-4292

Publication year2022

Publication date03/06/2022

Volume14

Issue number11

Article number2692

PublisherMDPI AG

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.3390/rs14112692

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/82622


Abstract

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.


Keywordsremote sensingaerial mappinghyperspectral imagingneural networks (information technology)unmanned aerial vehiclesgrasslandsgrassland farmingfeed production

Free keywordsdrone; remote sensing; hyperspectral; RGB; CNN; image transformer; silage production; grass sward


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-15-06 at 20:27