Mechanism of resistance to cyhalofop-butyl in Chinese sprangletop (Leptochloa chinensis (L.) Nees)

Yu, Jiaxing; Gao, Haitao; Pan, Lang; Yao, Zhenwei; Dong, Liyao

doi:10.1016/j.pestbp.2016.11.001

Cited by 45 publications

(47 citation statements)

References 39 publications

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“…Fenoxaprop‐ P ‐ethyl resistance in A. japonicus is a major challenge for farmers planting wheat and oilseed rape. In China, six weed species have evolved resistance to ACCase‐inhibiting herbicides, namely A. japonicus , A. aequalis , Beckmannia syzigachne , Sclerochloa kengiana , Leptochloa chinensis and Echinochloa crus‐galli . Among these six weeds, fenoxaprop‐ P ‐ethyl resistance in A. japonicus was the most frequently reported .…”

Section: Discussionmentioning

confidence: 99%

Fenoxaprop‐P‐ethyl resistance conferred by cytochrome P450s and target site mutation in Alopecurus japonicus

Chen

Zhang

et al. 2018

Pest Management Science

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Section: Discussionmentioning

confidence: 99%

Fenoxaprop‐P‐ethyl resistance conferred by cytochrome P450s and target site mutation in Alopecurus japonicus

Chen

Zhang

et al. 2018

Pest Management Science

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“…The studied field was naturally infested with Leptochloa chinensis Nees (L . chinensis) [ 17 ] and Cyperus iric [ 18 ] ( Fig 2 ), and the weed species were mixed in the field.…”

Section: Methodsmentioning

confidence: 99%

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Huang

Deng²,

Lan³

et al. 2018

PLoS ONE

140

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Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

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“…No obvious presence of diseases or insect infestations was observed in this field during the growth stages. The rice field was naturally infested with Chinese sprangletop ( L. chinensis ) [ 18 ] and Cyperus iric [ 19 ], as shown in Figure 2 . All these weeds can be treated with the same type of herbicide.…”

Section: Data Collectionmentioning

confidence: 99%

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery

Huang

Lan

Deng

et al. 2018

Sensors

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Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.

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Mechanism of resistance to cyhalofop-butyl in Chinese sprangletop (Leptochloa chinensis (L.) Nees)

Cited by 45 publications

References 39 publications

Fenoxaprop‐P‐ethyl resistance conferred by cytochrome P450s and target site mutation in Alopecurus japonicus

Fenoxaprop‐P‐ethyl resistance conferred by cytochrome P450s and target site mutation in Alopecurus japonicus

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery

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