Cereal weeds variation in middle Egypt: Role of crop family in weed composition

Alsherif, Emad A.

doi:10.1016/j.sjbs.2020.07.001

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…The current study revealed that community X contains weed flora more than other communities that reflect this community exhibited the characteristics of complex structure, more vegetation ecotypes and strong adaptability. Some weed species exhibited broad ecological amplitude, while others were restricted to specific types of environments with very narrow amplitude, suggesting that the recorded ecological amplitude of weed species differed from each other caused both by phenotypic plasticity and heterogeneity ( Alsherif, 2020 ). Portulaca oleracea is recognized as one of the worst weeds throughout the world and invades most subtropical and temperate agroecosystems, which occurs in almost all soil types and invades arable land quickly and is difficult to eradicate ( Visalakshy, 2007 ).…”

Section: Discussionmentioning

confidence: 99%

“…They consist of many more or less spatially discrete populations. Each population constitutes a potential source of reproduction that may generate new populations ( Grice, 1998 ), which will compete with jujube trees for soil nutrients and water, increase the temperature in the jujube orchard, and aggravate the damage of pests and diseases, cause yield reduction, and diminished quality ( Alsherif, 2020 ). Consequently, applying niche theory to study the competition between weeds, selecting plant resources with strong niches and no adverse effects on fruit tree growth to occupy preferentially the niche cannot only effectively inhibit the growth of other weeds, but also have a longer-lasting effect.…”

Section: Discussionmentioning

confidence: 99%

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Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Wang

Cao

Zou

et al. 2022

PeerJ

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Background Ziziphus jujuba Mill. cv. ‘LingwuChangzao’ is a traditional jujube cultivar in northwest China. It is of great significance to explore the weed community composition and environmental characterization for the ecological control and comprehensive management of weeds in jujube orchards. In this article, a total of 37 species were recorded in 40 sample plots (1 m × 1 m). Moreover, fourteen environmental indicators to characterize the spatial locations, climate and soil nutrient characteristics of the plant communities were adopted. Methodology Through the two-way indicator species analysis (TWINSPAN) quantity classification and canonical correspondence analysis (CCA) ranking methods, the types of weed communities in the main planting base of jujube ‘LingwuChangzao’ and the main environmental factors affecting the change and distribution of weed types were analyzed. Results The weed communities within the study area were divided into 15 types by the TWINSPAN classification. There were significant differences in soil factors to the species diversity indices of the weed communities, the diversity of weed communities was negatively correlated with available potassium, whereas positively correlated with soil water content. The CCA results showed that community structure and spatial distribution of weed communities were affected by soil water content, total potassium, soil organic carbon, total phosphorus, total nitrogen. Our results can be used as a reference for orchard weed management and provide a theoretical basis for weed invasion control and creating a higher biodiversity in arable land under the background of environmental change.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Wang

Cao

Zou

et al. 2022

PeerJ

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“…Among the challenges in crop production, weed control is ranked as one of the influencers of crop yield (Raja et al, 2020). Weeds are any undesirable plants that grow amongst agricultural crops or available surface interfering with crop growth and human activities (Alsherif, 2020). They compete with crops for valuable nutrients, water, sunlight, and carbon dioxide profoundly affecting farm productivity by invading crops and smother pastures and leading to significantly decrease in the quality of harvested crops (Milberg and Hallgren, 2004).…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

Hashemi-Beni

Gebrehiwot

Karimoddini

et al. 2022

Front. Remote Sens.

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Weeds are among the significant factors that could harm crop yield by invading crops and smother pastures, and significantly decrease the quality of the harvested crops. Herbicides are widely used in agriculture to control weeds; however, excessive use of herbicides in agriculture can lead to environmental pollution as well as yield reduction. Accurate mapping of crops/weeds is essential to determine weeds’ location and locally treat those areas. Increasing demand for flexible, accurate and lower cost precision agriculture technology has resulted in advancements in UAS-based remote sensing data collection and methods. Deep learning methods have been successfully employed for UAS data processing and mapping tasks in different domains. This research investigate, compares and evaluates the performance of deep learning methods for crop/weed discrimination on two open-source and published benchmark datasets captured by different UASs (field robot and UAV) and labeled by experts. We specifically investigate the following architectures: 1) U-Net Model 2) SegNet 3) FCN (FCN-32s, FCN-16s, FCN-8s) 4) DepLabV3+. The deep learning models were fine-tuned to classify the UAS datasets into three classes (background, crops, and weeds). The classification accuracy achieved by U-Net is 77.9% higher than 62.6% of SegNet, 68.4% of FCN-32s, 77.2% of FCN-16s, and slightly lower than 81.1% of FCN-8s, and 84.3% of DepLab v3+. Experimental results showed that the ResNet-18 based segmentation model such as DepLab v3+ could precisely extract weeds compared to other classifiers.

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Weeds as Alternate Organic Fertilizer in Garlic (Allium sativum L.) Production

Khan

Wahid

et al. 2022

Gesunde Pflanzen

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Cereal weeds variation in middle Egypt: Role of crop family in weed composition

Cited by 7 publications

References 28 publications

Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

Weeds as Alternate Organic Fertilizer in Garlic (Allium sativum L.) Production

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