Weed Detection Based on the Optimized Segmentation Line of Crop and Weed

Mao, Wenhua; Hu, Xiaoan; Zhang, Xiaochao

doi:10.1007/978-0-387-77253-0_27

Cited by 3 publications

(3 citation statements)

References 9 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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“…Performing weed control only where necessary (spot treatment) would be a more optimal solution. To achieve spot spraying in real time, many studies are underway to develop sensors capable of discriminating weeds from crop plants, using different strategies; notably, machine vision (Wang et al 2007), visible spectrum analysis (Mao et al 2008), reflectance (Paap et al 2008), or fluorescence (Longchamps et al 2010). The diversity of weed phenotypes, shapes, colors, and textures, but also their taxonomic proximity with some crops, makes real-time discrimination unprofitable because of signal processing time or classification inaccuracy.…”

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confidence: 99%

Could Weed Sensing in Corn Interrows Result in Efficient Weed Control?

et al. 2012

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At the field scale, weeds generally appear aggregated rather than randomly distributed, and this aggregation is linked to the spatial heterogeneity of biotic and abiotic factors. Crop management practices shape the spatial pattern of weed infestations by modifying certain factors having an impact on weed emergence and growth. Although crop seeding is often the last in-field disturbance before crop and weed emergence, its effect on the distribution of weeds has received little attention in the literature. The purpose of this study was to assess the influence of the planting operation on weed cover and presence in corn fields using digital images to investigate the possibility of sensing the interrow to infer the presence or absence of weeds on the corn row. A total of 18 site-years under conventional tillage treated with a single POST application of herbicide were selected across seven locations. Image analysis, at the V2 to V4 growth stage of corn, was used to compare the weed cover in three zones: the undisturbed interrows, the corn rows, and the interrows compacted by tractor wheel traffic. For 61% of site-years, there was no significant difference among the zones. When there was a significant difference compared with the other two zones, the undisturbed interrow was usually less infested. Point-to-point comparisons of weed presence or absence (based on a threshold of five pixels) between the interrow and the corn row revealed 70 or 73% correspondence, depending on the type of interrow (undisturbed or tracked). However the error of inference of the corn row weed cover generated by sensing only adjacent interrows may be too high for efficient commercial weed control.

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“…The crop and weed discrimination methods can be classified into four main categories which are spectrum analysis (Mao et al, 2008;Tyystjarvi et al, 2011), morphological comparison (Perez et al, 2000;Rumpf et al, 2012;Tang 2002), texture and frequency analysis (Bossu et al, 2009;Sabeeniana and Palanisamyb, 2009;Tang, 2002) and the spacial location distinguishing . The first three methods are not only for crops and weeds discrimination, but also widely studied for weed classification.…”

Section: The Crop and Weed Discrimination Methodsmentioning

confidence: 99%

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Liu

Lee

Saunders

2014

American Journal of Agricultural and Biological Sciences

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More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the notillage background than the existing methods and could be used as a powerful tool for the weed control.

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Weed Detection Based on the Optimized Segmentation Line of Crop and Weed

Cited by 3 publications

References 9 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

Could Weed Sensing in Corn Interrows Result in Efficient Weed Control?

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

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