Selection of the most efficient wavelength bands for discriminating weeds from crop

Piron, Alexis; Leemans, Vincent; Kleynen, Olivier; Lebeau, Frédéric; Destain, Marie‐France

doi:10.1016/j.compag.2007.12.007

Cited by 48 publications

(28 citation statements)

References 19 publications

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“…Up to date, there are many studies on identification of weeds from crops using the sensitive spectral bands with encouraging results. However, the identification accuracy is low in cases when the spectral difference between the crop and the weed is not obvious, or the reflection of leaves is affected by factors of water content, plant disease, and growth stage [9][10][11][12][13]. Therefore, to more effectively discriminate weeds from crops, the combination of multiple features, such as the combination of shape and textural, shape and spectral, and spectral and textural features, should be considered.…”

Section: Introductionmentioning

confidence: 99%

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

et al. 2017

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Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

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Section: Introductionmentioning

confidence: 99%

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

et al. 2017

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“…The best discrimination was obtained using a combination of the logarithmic transformation and PLS-LDA method . Borregaard et al (2000) and Piron et al (2008) estimated the effective wavelengths for discriminating carrots from weeds, and potatoes from various weed leaves, under artificial lighting.…”

Section: Crop Plant and Weed Discriminationmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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“…The multispectral acquisition device comprised a black and white camera (C-cam BCI 5 1.3 megapixels) coupled with a filter wheel holding 22 interference filters covering the visible and near infrared spectrum (400-1000 nm approximately) (Piron et al, 2008a).…”

Section: Multispectral and Stereoscopic Acquisition Methodsmentioning

confidence: 99%

“…In Piron et al (2008a), it was shown that the best combination of filters for detecting various weed species located within carrot rows were respectively centered on 450, 550 and 700 nm.…”

Section: Combination Of Multispectral and Stereoscopic Informationmentioning

confidence: 99%

“…Zhang and Chaisattapagon (1995) selected five filters to discriminate five species of weeds from soil and wheat at two growth stages, on potted plants located under artificial lighting. Piron et al (2008a) used a combination of three wide band interference filters to detect weeds located within carrots rows and found a classification accuracy of only 72%.…”

Section: Introductionmentioning

confidence: 99%

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Improving in-row weed detection in multispectral stereoscopic images

Piron

Leemans

Lebeau

et al. 2009

Computers and Electronics in Agriculture

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a b s t r a c tPrevious research has shown that plant height and spectral reflectance are relevant features to classify crop and weeds in organic carrots: classification based on height gave a classification accuracy (CA) of up to 83% while classification based on a combination of three multispectral bands gave a CA of 72%.The first goal of this study was to examine the simultaneous use of both height and multispectral parameters. It was found that classification rate was only slightly improved when using a feature set comprising both height and multispectral data (2%).The second goal of this study was to improve the detection method based on plant height by setting an automatic threshold between crop and weeds heights, in their early growth stage. This threshold was based on crop row determination and peak detection in plant height probability density function, corresponding to the homogeneous crop population. Using this method, the CA was 82% while the CA obtained with optimal plant height limits is only slightly higher at 86%.

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Selection of the most efficient wavelength bands for discriminating weeds from crop

Cited by 48 publications

References 19 publications

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Improving in-row weed detection in multispectral stereoscopic images

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