Screening of weeds and their effect on alfalfa (Medicago sativa)

MSEDDI, KHALIL; ALGHAMDI, AHMED; SHARAWY, SHARIF; IBRAHIM, NASER

doi:10.56093/ijas.v87i11.75795

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Furthermore, certain weed species, such as perilla mint ( Perilla frutescens L.), are toxic to livestock 7 . As the planting area increases, the number of species and quantities of weeds in alfalfa fields also increase, 8 thus severely impairing the quality of alfalfa hay.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Yang,

Chen,

2024

Pest Management Science

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BACKGROUNDAccurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa.RESULTSA novel neural network, ResNet‐101‐segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet‐101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet‐101), ResNet‐101‐segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets.CONCLUSIONResNet‐101‐segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet‐101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Yang,

Chen,

2024

Pest Management Science

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Inaccessible Zones of Jabal Salma, Ha’il Region in Saudi Arabia: A Reservoir for Native Seed Species

Alghamdi¹,

Mseddi²,

Abdelgadir³

et al. 2018

JEBAS

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A reservoir in the current study describes where endemic plants are most likely to thrive and provide natural gene bank for native seed species. Twelve sites representing different altitudinal zones of Jabal Salma in Ha'il region, Saudi Arabia were regularly visited for two consecutive years. From each site 2-5 locations were investigated for floristic composition and types of vegetation. During the study, the presence of total 150 species representing 39 families was recorded. Among the reported species, the highest number of species (25 species) were belong to the family Asteraceae which was followed by the family Brassicaceae, (17 species), Fabaceae (14 species), Boraginaceae (11 species), Caryophyllaceae (10 species) and finally Poaceae (9 species). However, 15 other families including Acanthaceae, Convolvulaceae, Moraceae, Nyctaginaceae and Primulaceae, were monospecific which represented by only a single species. In addition, the effect of elevation on plant species distribution along altitudinal gradient of Jabal Salma has been noticed clearly in this study.

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Imazamox selectivity for alfalfa crop

Jovanović-Radovanov¹,

Radivojević²,

Stojanović³

2018

Acta herbologica

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Tokom 2016. godine izvedena su ispitivanja selektivnosti primene imazamoksa (preparata Sinamon i Pulsar-40) u usevu lucerke. Ogledi su postavljeni na dva lokaliteta, Boljevci i Kovilovo, po potpuno slučajnom blok sistemu u četiri ponavljanja. Ispitivani preparati primenjeni su u maksimalno preporučenim i dvostruko većim količinama primene. Vizuelne ocene eventualnih simptoma fitotoksičnosti obavljene su dve i četiri nedelje od momenta primene herbicida. U prvoj oceni, simptomi fitotoksičnosti (u vidu blage hloroze), utvrđeni su samo na lokalitetu Boljevci pri maksimalno preporučenim količinama primene. Dvostruko veće količine primene izazvale su nešto intezivniju hlorozu, pri čemu nivo fitotoksičnosti nije prešao 10%. U drugoj oceni, ni na jednom od lokaliteta, nisu bili vidljivi simptomi fitotoksičnosti. Za kvantitativne pokazatelje prinosa (prinos sveže mase, prinos suvog sena i sadržaj korova u zelenoj masi) i kvalitativne pokazatelje prinosa lucerke (sadržaj suve materije, sadržaj sirovih proteina i hemijski pokazatelji kvaliteta kompozitnih uzoraka) nije utvrđen statistički značajan uticaj imazamoksa ni na jednom od lokaliteta. Ključne reči: lucerka, imazamoks, selektivnost, kvalitativni i kvantitativni parametri prinosa. UVOD Lucerka (Medicago sativa L.) je najstarija i najznačajnija krmna biljka u svetu (Bagavathiannan and Van Acker, 2009), kako sa aspekta površina koje zauzima tako i u pogledu visine, kvaliteta i energetske vrednosti prinosa (Milić i sar., 2016). Gaji se na svim kontinentima, a široka geografska rasprostranjenost lucerke uslovljena je njenom lakom adaptibilnošću na različite klimatske i zemljišne uslove. Prema podacima statističkog godišnjaka Srbije, površine pod lucerkom u Srbiji zauzimaju oko 107430 ha, sa prosečnim prinosom sena od 5,7 t ha-1 (SGS,

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Screening of weeds and their effect on alfalfa (Medicago sativa)

Cited by 3 publications

References 24 publications

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Inaccessible Zones of Jabal Salma, Ha’il Region in Saudi Arabia: A Reservoir for Native Seed Species

Imazamox selectivity for alfalfa crop

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