Weed image classification using Gabor wavelet and gradient field distribution

Aris, Ishak; Hussain, Aini; Mustafa, Mohd. Marzuki

doi:10.1016/j.compag.2008.12.003

Cited by 78 publications

(28 citation statements)

References 6 publications

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“…Automatic evaluation of the density of broad-leaved weeds in digital images of a cereal field could be done with a simple algorithm, although the result overestimated infestation in clean areas and underestimated infestation in highly infested spots (Berge et al 2008). Broad-leaved weeds, mainly dicots, can be identified among grass using color, morphological and texture features (Gebhardt and Kühlbauch 2007;Ishak et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Automatic identification of crop and weed species with chlorophyll fluorescence induction curves

et al. 2010

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Automatic identification of crop and weed species is required for many precision farming practices. The use of chlorophyll fluorescence fingerprinting for identification of maize and barley among six weed species was tested. The plants were grown in outdoor pots and the fluorescence measurements were done in variable natural conditions. The measurement protocol consisted of 1 s of shading followed by two short pulses of strong light (photosynthetic photon flux density 1700 lmol m -2 s -1 ) with 0.2 s of darkness in between. Both illumination pulses caused the fluorescence yield to increase by 30-60% and to display a rapid fluorescence transient resembling transients obtained after long dark incubation. A neural network classifier, working on 17 features extracted from each fluorescence induction curve, correctly classified 86.7-96.1% of the curves as crop (maize or barley) or weed. Classification of individual species yielded a 50.2-80.8% rate of correct classifications. The best results were obtained if the training and test sets were measured on the same day, but good results were also obtained when the training and test sets were measured on different dates, and even if fluorescence induction curves measured from both leaf sides were mixed. The results indicate that fluorescence fingerprinting has potential for rapid field separation of crop and weed species.

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Section: Introductionmentioning

confidence: 99%

Automatic identification of crop and weed species with chlorophyll fluorescence induction curves

et al. 2010

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“…The name "co-occurrence matrix" is used for the resulting matrix of conditional probabilities created for all combinations of each pair of co-occurring grayscale or color values at the defi ned spatial offset and direction. In the majority of the published studies investigating the use of texture for weed detection, the images contained a monoculture (i.e., a single species per image) and classifi cation rates of 90 % or more were frequently obtained (Shearer and Holmes 1990 ;Meyer et al 1998 ;Burks et al 2002 ;Tang et al 2003 ;Ishak et al 2009 ).…”

Section: Plant Recognition: Using Texturementioning

confidence: 98%

The Biological Engineer: Sensing the Difference Between Crops and Weeds

Slaughter

2013

Automation: The Future of Weed Control in Cropping Systems

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This chapter describes the current state of the art in technology and methodology being used to develop sensors for automated weed control in cropping systems. The development of a reliable universal weed vs. crop plant sensor that works well in a wide range of crops and cropping systems is a formidable task. The discussion in this chapter highlights the signifi cant progress that has been made in developing new, more robust, automatic sensing systems that can differentiate crop plants from weeds. Case studies documenting high levels of success in trials conducted outdoors in the natural, largely uncontrolled environment of an agricultural cropping system are presented. A discussion of the strengths and current challenges of the more successful weed and crop sensing techniques is provided. In many cases the methodology has utilized site-or condition-specifi c a priori knowledge to make the sensors smarter in a local context. This chapter highlights the advantages and compromises made in using these techniques. The chapter concludes with a discussion of the remaining engineering challenges to the development of a comprehensive, multifaceted fusion of several methods for sensing the differences between crops and weeds across the entire crop production cycle, and how the rapid development of advanced sensing and machine learning technologies will facilitate new plant recognition architectures and systems to achieve the level of machine recognition of weeds needed for automated weed control in cropping systems.

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“…Herbs identification researches based on leaves characteristics have been proposed in various techniques and systems. This is because leaves have unique characteristics, such as shape, color, texture, and the nature of the odors [2][3][4][5]. However, dealing with herbs in the same group of family is more challenging than with different family groups since their physical appearances and aroma may be similar.…”

Section: Introductionmentioning

confidence: 99%