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Lookup NU author(s): Dr John Favier
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Optical spectral reflectance and image analysis techniques were investigated as possible solutions to discriminate crop and weed plants. The range of plants included two brassica crop species (cabbage and calabrese), a cereal crop (barley) and eight weed species (chickweed, charlock, wild radish, canola, shepherd's purse, fat hen and wild oat). Spectral signatures were obtained from optical reflectance measurements taken with a spectrophotometer in reflectance mode in the region between 700 and 1350 nm. Algorithms were developed based on multivariate statistical analysis of the plant reflectance spectra. By minimizing wavebands of interest for certain crop/weed combinations, better than 95% discrimination accuracy was attained for only two or three waveband measures. Using filters at these wavebands it was possible to easily segregate crop from weed plants in an image. Discrimination on the basis of leaf texture was investigated using textural signatures for whole leaves derived from a gray level co-occurrence matrix of nearest-neighbour pixel intensity. Textural features of leaves were expressed in the form of feature vectors comprising nine textural parameters extracted from the co-occurrence matrix. A numerical Bayesian classifier was used to classify leaves based on minimum distance between a mean feature vector determined from a training set and the test feature vector. A mean discrimination accuracy of 90% was achieved between all plant species and almost 100% separation was achieved between the crop and weeds. The results show that a combination of spectral imaging and texture analysis may provide a robust method of discrimination with potential for real time application.
Author(s): Favier J, Ross DW, Tsheko R, Kennedy DD, Muir AY, Fleming J
Publication type: Article
Publication status: Published
Journal: Proceedings of SPIE - The International Society for Optical Engineering
Year: 1999
Volume: 3543
Pages: 311-318
Print publication date: 01/01/1999
ISSN (print): 0277-786X
ISSN (electronic):
