Abstract The accurate detection and treatment of weeds in agricultural fields is a necessary procedure for managing crop yield and avoiding herbicide pollution. With the emergence of unmanned aerial vehicles (UAV), the ability to acquire spatial data at the desired spatial and temporal resolution became available, and the resulting input data met high standards for weed management. In this paper, we tested four independent classification algorithms for the creation of weed maps, combining automatic and manual methods, as well as object-based and pixel-based classification approaches, which were used separately on two subsets. Input UAV data were collected using a low-cost RGB camera due to its affordability compared to multispectral cameras. Classification algorithms were based on the random forest machine learning algorithm for weed and bare soil extraction, following an unsupervised classification with the K-means algorithm for further estimation of weeds and bare soil presence in non-weed and non-soil areas. Of the four classification algorithms tested, the automatic object-based classification method achieved the highest classification accuracy, resulting in an overall accuracy of 89.0% for subset A and 87.1% for subset B. Automatic classification methods were robustly developed, using at least 0.25% of the scene size as the training data set in all circumstances anticipated for the random forest classification algorithm to operate. The use of the algorithm resulted in weed maps consisting of zoned classes and covering areas with similar biological properties, making them ready for use as inputs in weed treatments that use agricultural machinery.

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基于无人机影像的燕麦田杂草自动测绘方法

摘要 准确检测和处理农田杂草是管理作物产量和避免除草剂污染的必要程序。随着无人机 (UAV) 的出现，以所需的空间和时间分辨率获取空间数据的能力变得可用，并且由此产生的输入数据符合杂草管理的高标准。在本文中，我们测试了四种用于创建杂草图的独立分类算法，结合了自动和手动方法，以及基于对象和基于像素的分类方法，分别用于两个子集。由于与多光谱相机相比价格低廉，因此使用低成本 RGB 相机收集输入无人机数据。分类算法基于用于杂草和裸土提取的随机森林机器学习算法，采用 K 均值算法进行无监督分类，以进一步估计非杂草和非土壤区域中杂草和裸土的存在。在测试的四种分类算法中，基于对象的自动分类方法实现了最高的分类准确率，导致子集 A 的总体准确率为 89.0%，子集 B 的总体准确率为 87.1%。 自动分类方法得到了稳健的发展，至少使用了 0.25%场景大小作为随机森林分类算法在预期运行的所有情况下的训练数据集。该算法的使用导致杂草图由分区类组成并覆盖具有相似生物特性的区域，