A joint article between Finnish Geospatial Research Institute (FGI) and Luke Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone has won the 2019 Best Paper Award of MDPI Agriculture.
The article discusses how novel drone-based remote sensing technology could be utilized in many phases of silage production. The objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data.
“Silage is he main feed in milk and ruminant meat production in Northern Europe. Grass swards are harvested three times in season, and fertilizer is applied similarly three times. Therefore, timely information of the yield is necessary several times in a season for making decisions on harvesting time and rate of fertilizer application,” says senior scientist Oiva Niemeläinen from Luke.
“Our research results were extremely promising, showing that the proposed multispectral photogrammetric approach can provide accurate biomass estimates of grass swards, and could be developed as a low-cost tool for practical farming applications. Tha can hold significant benefits for agricultural entrepreneurs.”
From Luke, the authors included research scientist Jere Kaivosoja and senior scientist Oiva Niemeläinen. FGI’s authors were Niko Viljanen, Eija Honkavaara, Roope Näsi, and Teemu Hakala.