Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

Kirsi Karila*, Raquel Alves Oliveira, Johannes Ek, Jere Kaivosoja, Niko Koivumäki, Panu Korhonen, Oiva Niemeläinen, Laura Nyholm, Roope Näsi, Ilkka Pölönen, Eija Honkavaara

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number2692
Pages (from-to)1-21
Number of pages21
JournalRemote Sensing
Volume14
Issue number11
DOIs
Publication statusPublished - 1 Jun 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • CNN
  • drone
  • grass sward
  • hyperspectral
  • image transformer
  • remote sensing
  • RGB
  • silage production

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