Estimating grass sward quality and quantity parameters using drone remote sensing with deep neural networks

HIGHLIGHTS

• who: Kirsi Karila and colleagues from the Department of Applied Physics, School of Science, Aalto University, Espoo, Finland have published the research: Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks, in the Journal: (JOURNAL) of 14/04/2022
• what: The aim 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 and hyperspectral images (HSI) and compare the results with the random forest (RF) method and handcrafted features. By changing the last . . .
  

   

  

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