Vegetation extraction in the field using multi-level features

Accurate and automatic vegetation extraction from digital plant images in the field is a widely studied topic in precision agriculture. Many techniques focus on pixels or regions to be segmented as plants or back-grounds, such as colour index-based and learning-based methods. Different from a traditional two-class classification problem, the proposed method regarded vegetation extraction as a multi-class task. In consideration of manually annotated errors at the edge of a plant image, the original marked mask was re-labelled using a Gaussian probability function. To capture more adequate information in the process of feature extraction, 9 pixel-level colour features and 18 region-level statistical characteristics of neighbourhood pixels were computed from three colour spaces. The extracted 27-dimensional features were inputs of a classification model, which output multi-class labels. A suitable threshold was finally selected to obtain the segmented image. Experimental results showed that the proposed multi-class and multi-level features (MCMLF) method achieved better performance than the other approaches. Through the quantitative and qualitative analysis of segmentation results, it was also found that the suggested method had high computation efficiency as well as strong adaptation ability to solve the outdoor challenges, including various lighting conditions, shadow regions, and complex backgrounds.