Abstract Weeds are normally sprayed uniformly across field irrespective of their density in the field. Automatic methods can be developed that help us to locate and spray weed according to its density. Automatic weed detection is necessary for precision spraying that can improve rice yield and reduce production cost. This aids farmers to get an idea of local weed coverage and spray only the weed infested areas of field. This paper proposes two classification techniques to distinguish images based on their weed density. The weed covered by our work is of category grass and is called nutgrass in rice. We aim to classify images into three classes according to grass density. The first technique uses texture features extracted from gray level co-occurrence matrix (GLCM) and produces an accuracy of 73% using Radial Basis Function (RBF) kernel in Support Vector Machine (SVM). Another technique proposed uses features such as moments that are invariant to scale and rotation to classify grass density. The second technique outperforms the first one with an accuracy of 86% using Random Forest classifier. A comparison of these two techniques in terms of execution timing is also conducted. These techniques are implemented in MATLAB and tested on a dataset of hundred images that are collected from an actual rice field.

中文翻译：

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基于计算机视觉的水稻杂草密度分类

摘要 杂草通常在田间均匀喷洒，而与田间密度无关。可以开发自动方法来帮助我们根据杂草密度定位和喷洒杂草。精准喷药需要自动杂草检测，可以提高水稻产量，降低生产成本。这有助于农民了解当地的杂草覆盖率，并只喷洒杂草出没的田间区域。本文提出了两种分类技术来根据图像的杂草密度来区分图像。我们的工作所涵盖的杂草属于草类，在水稻中被称为 nutgrass。我们的目标是根据草密度将图像分为三类。第一种技术使用从灰度共生矩阵 (GLCM) 中提取的纹理特征，并使用支持向量机 (SVM) 中的径向基函数 (RBF) 内核产生 73% 的准确率。提出的另一种技术使用诸如对尺度和旋转不变的矩等特征来对草密度进行分类。第二种技术使用随机森林分类器以 86% 的准确率优于第一种技术。还对这两种技术在执行时序方面进行了比较。这些技术在 MATLAB 中实现，并在从实际稻田收集的数百幅图像的数据集上进行测试。第二种技术使用随机森林分类器以 86% 的准确率优于第一种技术。还对这两种技术在执行时序方面进行了比较。这些技术在 MATLAB 中实现，并在从实际稻田收集的数百幅图像的数据集上进行测试。第二种技术使用随机森林分类器以 86% 的准确率优于第一种技术。还对这两种技术在执行时序方面进行了比较。这些技术在 MATLAB 中实现，并在从实际稻田收集的数百幅图像的数据集上进行测试。