Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
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DOI：
10.1016/j.compag.2017.05.018
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发表时间：
2017-08-01
影响因子：
8.3
通讯作者：
Fox, C. W.
中科院分区：
文献类型：
--
作者：
Binch, A.;Fox, C. W.
自动化机器人除草将提高奶牛场和绵羊场的生产力，同时有助于保护环境。以前的研究报告了机器视觉方法将草从草地杂草中分离出来的结果，但每个人都使用自己的数据集，并且只报告自己算法的性能，因此无法比较它们。一个明确的，大规模的独立研究提出了所有主要的已知草原杂草检测方法评估一个新的标准化数据集下的更广泛的环境条件。这使得第一次对这些方法和未来的方法进行公平、无偏见、独立和统计上显著的比较。我们测试的功能，包括线性二进制模式，BRISK，傅立叶和分水岭;和分类器，包括支持向量机，线性判别，最近邻，和元分类器组合。最准确的方法是使用线性二进制模式与支持向量机。(1)（C）2017 Elsevier B. V.保留所有权利。
Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine.(1) (C) 2017 Elsevier B.V. All rights reserved.