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.

中文翻译：

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使用多级特征的野外植被提取

从野外数字植物图像中准确自动提取植被是精准农业中一个广泛研究的课题。许多技术侧重于要分割为植物或背景的像素或区域，例如基于颜色索引和基于学习的方法。与传统的二类分类问题不同，该方法将植被提取视为多类任务。考虑到植物图像边缘的人工标注错误，使用高斯概率函数重新标记原始标记的掩码。为了在特征提取过程中获取更充分的信息，从三个颜色空间计算了9个像素级颜色特征和18个邻域像素的区域级统计特征。提取的 27 维特征是分类模型的输入，该模型输出多类标签。最后选择一个合适的阈值来获得分割图像。实验结果表明，所提出的多类和多级特征（MCMLF）方法比其他方法取得了更好的性能。通过对分割结果的定量和定性分析，还发现所提出的方法具有较高的计算效率和较强的适应能力，可以解决户外挑战，包括各种光照条件、阴影区域和复杂背景。实验结果表明，所提出的多类和多级特征（MCMLF）方法比其他方法取得了更好的性能。通过对分割结果的定量和定性分析，还发现所提出的方法具有较高的计算效率和较强的适应能力，可以解决户外挑战，包括各种光照条件、阴影区域和复杂背景。实验结果表明，所提出的多类和多级特征（MCMLF）方法比其他方法取得了更好的性能。通过对分割结果的定量和定性分析，还发现所提出的方法具有较高的计算效率和较强的适应能力，可以解决户外挑战，包括各种光照条件、阴影区域和复杂背景。