Abstract
Cover crops have many benefits such as reducing chemical materials, improving soil condition, preventing soil erosion and conservation of soil water. The long-term objective of this study is to assess and estimate cover crop effectiveness such as green manure, weed depression and soil conservation. In this paper, spatial distribution of herb species and herbage mass was estimated.
A ground based hyperspectral imaging, which is useful for acquiring field information, was employed to monitor the cover crop field (bristle oat and hairy vetch). In order to generate the maps of herbage mass, first, plant portions were extracted from hyperspectral images by NDVI (normalized difference vegetation index) threshold. Next, they were classified into plant species using linear discriminant models. Finally, the herbage mass of each plant species was estimated individually using partial least squares regression model, and mapped with gradient colors depending on the estimated value.
The results show that the success rate of plant area extraction was 100% and the success rate of the plant species classification was 78.7%. With regards to the result of the herbage mass estimation, the model that used both plant pixel spectra and plant cover rate as explanatory variables had the highest and most stable accuracy than the other models. The maps of plant species and herbage mass reflected the actual spatial distribution on the field. It was demonstrated that the hyperspectral imaging system developed in this study is a useful technique for monitoring the cover crop.
