Identification of plants in meadows by remote sensing
Abstract
Raw milk accounts for about 10% of the agricultural output in Japan. Despite growing demand for raw milk, milk production has not shown much increase. For resolving this situation, there is a need to improve the productivity of raw milk. Self-sufficiency rate of livestock products is calculated with feed self-sufficiency rate. However, this rate for daily is declining year by year due to the high supply of imported concentrates feed. Therefore, its necessary to increase grass self-sufficiency, however, about half of the meadows in Hokkaido are occupied by weeds. That caused by most of the weeds look very similar to the grass and are very difficult to distinguish visually. In the previous studies, though the classification into weeds and grass with the remote sensing technology has been tried, its not successful since the index based on the measurement using wide band filters with FWHM of 50-100nm. The purpose of this study is to establish a method to make a weed distribution map for meadows based on remote sensing with drone or satellite, using narrow band filters with FWHM of 10nm . The spectral reflectance for 6 species of grass and weeds, which are common in meadows in Hokkaido, was measured by a hyperspectral camera and a portable spectrometer at relatively higher wavelength resolution of ~4nm in the range of 420-840nm. A new method to identify those 6 species using 4 narrow bands, selected from whole measured wavelengths, was suggested by the analysis of these hyper spectral data. For the next step, a multi-band camera with the four selected filters will be onboard drones. The accuracy of the identification for 6 species of grass and weeds using the four narrow bands of reflectance were evaluated. The spectral reflectance was normalized by the values at 660 nm and 800 nm to emphasize the difference of the spectrum. It is found that those 6 species can be identified based on the measurement with four narrow band filters.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.B25E1512I