Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland

Binch, Adam and Fox, Charles (2017) Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland. Computers and Electronics in Agriculture, 140 . pp. 123-138. ISSN 0168-1699

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Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
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Abstract

Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms
7 while helping to conserve their environments. Previous studies have reported results of machine
8 vision methods to separate grass from grassland weeds but each use their own datasets and
9 report only performance of their own algorithm, making it impossible to compare them. A
10 definitive, large-scale independent study is presented of all major known grassland weed detec-
11 tion methods evaluated on a new standardised data set under a wider range of environment
12 conditions. This allows for a fair, unbiased, independent and statistically significant comparison
13 of these and future methods for the first time. We test features including linear binary pat-
14 terns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear
15 discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method
16 is found to use linear binary patterns together with a support vector machine

Keywords:agriculture, robotics
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
ID Code:32031
Deposited On:27 Jun 2018 21:19

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