Crop and weed classification based on a colour and NIR sensory setup

Publisher:
Whittles Publishing
Publication Type:
Conference Proceeding
Citation:
Proc. Innovative Production Machines and Systems - 5th I*PROMS Virtual International Conference, 2010, pp. 212 - 217
Issue Date:
2010-01
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This paper presents a multi-modal sensing approach for detecting Bidens pilosa L (commonly known as cobbler's peg) in wheat farms. Bidens is an annual broad leaf weed in tropical and sub-tropical regions and reported to be a weed that needs to be identified and eliminated when farming thirty one different crop varieties. Both Bidens and wheat leaves can have similar visual cues due to the curled up nature of the wheat leaves. This makes a straightforward visual image (RGB) based classification nontrivial. Therefore, we have integrated another informative band in the spectrum, which is the NIR band. Information retrieved is processed to generate a series of cues that are then fed into a classification algorithm. Bidens and wheat plant species are used to verify the classification algorithm. The proposed technique is able to achieve an accuracy of 88% - 95% even when there is substantial overlapping between Bidens and wheat leaves.
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