Abstract
Grassland degradation estimation is essential to
prevent global land desertification and sandstorms.
Typically, the key to such estimation is to measure
the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In
contrast, deep learning-based image segmentation
algorithms are potentially capable of automatic
assessment of the coverage of indicator plants.
Nevertheless, a suitable image dataset comprising
grassland images is not publicly available. To
this end, we build an original Automatic Grassland
Degradation Estimation Dataset (AGDE-Dataset),
with a large number of grassland images captured
from the wild. Based on AGDE-Dataset, we are
able to propose a brand new scheme to automatically estimate grassland degradation, which mainly
consists of two components. 1) Semantic segmentation: we design a deep neural network with an
improved encoder-decoder structure to implement
semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide
the estimation of grassland degradation based on
the results of semantic segmentation. Experimental results show that the proposed method achieves
satisfactory accuracy in grassland degradation estimation