ABSTRACT

In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

摘要

在这项研究中，我们提出了一种使用商业无人飞行器（UAV）和红绿蓝（RGB）相机以及简单线性迭代聚类（SLIC）算法和随机森林（ RF）分类器。在SLIC-RF算法中，我们评估了输入特征的不同组合：三种颜色空间（RGB，色相-饱和度[HSV]，CIE-L * a * b），树冠高度模型（CHM），空间纹理（纹理）和四个植被指数（VI）（过量的绿色[ExG]，过量的红色[ExR]，绿红色的植被指数[GRVI]和植被提取的颜色指数[CIVE]）。在颜色空间中，基于HSV的SLIC-RF模型显示出最佳性能，并且具有最高的袋外（OOB）精度（0.904）。通过将HSV与CHM，Texture，ExG或CIVE结合使用，可以提高分类准确性。通过HSV + Texture组合可获得最高的OOB精度（0.915）。来自混淆矩阵的最大错误发生在农作物与杂草之间的分类中，而土壤的分类可以非常高精度。这些结果表明，利用本研究开发的SLIC-RF算法，即使在水稻生长初期，也可以用可接受的精度通过消费级无人机图像区分稻米和杂草，从而满足特定地点杂草管理（SSWM）的需求。小水稻植物..