BACKGROUND Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. RESULTS We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the "bccr-segset" dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. CONCLUSIONS The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection.

中文翻译：

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一种基于轮廓蒙版和滤波后的本地二进制模式算子的形态相似的农作物和杂草检测新方法。

背景技术杂草是农业生产率低下的主要原因。一些杂草的形态特征与农作物相似，因此难以区分。结果我们提出了一种新方法，该方法结合使用结合了局部二值模式算子的滤波特征和植物叶轮廓蒙版提取的特征来提高阔叶植物之间的鉴别率。使用打开和关闭形态运算符来过滤植物图像中的噪声。使用试验床系统收集生长的四个阶段的图像。基于掩模的局部二进制图案特征与滤波后的特征和系数k相结合。使用具有径向基函数核的支持向量机对农作物和杂草进行分类。通过研究最佳参数，该方法在在线发布的“ bccr-segset”数据集中具有4个类别，分类精度达到98.63％，而以前报道的方法则达到91.85％。结论所提出的方法增强了对具有相似外观的农作物和杂草的鉴定，并证明了其在实时杂草检测中的能力。