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A method for estimating the height of Achnatherum splendens based on image processing
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AI
DOI：10.1016/j.compag.2024.109226.png)
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Abstract
En 中文
The growth of Achnatherum splendens affects the growth of dominant forage grasses in grasslands, which leads to a decrease in grassland biomass and an increase in grassland ecological environment degradation. It is a time and labor-consuming work to measure the height of Achnatherum splendens in wild grassland, so developing an intelligent grass height estimation method is indispensable. This study aims to propose a novel method for estimating the height of Achnatherum splendens based on image processing. The mobile ground robot with the binocular depth camera captures image information from different angles. This method acquires the edge features of the image from different angles and obtains the height pixel points by feature matching and clustering. The pixel points filtered by deep learning models' Bounding Box are converted to the world coordinate system using spatial geometry. This study took YOLOv7-X as an example, as its average precision (AP) detection rate was 96.6 %, which was superior to other deep learning algorithms in terms of overall performance. In the mean height estimation experiment of 10 test sites, the mean relative error (MRE) was 6.4 % and coefficient of determination (R2) was 0.74. In the mean height estimation experiment of 10 test sites, the MRE was 3.1 % and the R2 was 0.85. It is proven that this proposed method is effective and strongly agrees with actual Achnatherum splendens heights measured manually. Experiments also showed that this method performed better in the small sample test sites (The basal coverage was no more than 1 m2 in this study). This study encapsulated the above methods and proposed a model called ASHS, which could estimate the height of grassland vegetation represented by Achnatherum splendens. Different algorithm modules in the model can be replaced according to the vegetation situation. This study may contribute to estimating the quadrat height of grasses and developing intelligent robots in grassland resource surveys.
Keywords:
Achnatherum splendens
Height estimation
Deep learning
Coordinate transformation
Feature matching
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