Leaf and stem anatomical characterization of <i>Euphorbia hirta</i> L., a tolerant species to glyphosate

Chapeta, Ana Carolina Oliveira; Tozin, Luiz Ricardo dos Santos; Souza, Amanda dos Santos; Costa, Milena Gonçalves; Leal, Jéssica Ferreira Lourenço; Pinho, Camila Ferreira de

doi:10.1080/03601234.2023.2177462

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…At present, two methods are mainly used for fruit target detection. One is the traditional image detection and segmentation technology that mainly uses color ( Janani and Jebakumar, 2023 ; Tajdar et al., 2023 ), texture ( Alshehhi and Marpu, 2023 ; Chapeta et al., 2023 ), edge ( Xie et al., 2022 ; Quan et al., 2023 ), and other feature information. However, the shallow features can only detect the target in a limited scene, and these methods often lack generalizability and robustness.…”

Section: Introductionmentioning

confidence: 99%

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

et al. 2023

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IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments.ResultsThe experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops.DiscussionThe results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

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Section: Introductionmentioning

confidence: 99%

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

et al. 2023

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“…Previous studies have also shown the use of anatomical markers in correlation with morphological markers (Yousaf et al, 2008). A lot of research has been done on anatomy of different plant species (Tripathi et al, 2023;Chapeta et al, 2023) but there are limited studies at intraspecific level of spinach plant species. Therefore, an attempt has been made to examine different accessions of spinach by leaf epidermal anatomical markers, in association with morphological markers.…”

Section: Introductionmentioning

confidence: 99%

Taxonomic Evaluation of Spinacia oleracea L. Accessions by Morphological and Anatomical Markers

Iftikhar,

Rashid,

Yousaf

et al. 2023

J. Plant Environ.

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Spinacia oleracea L. is an edible crop and considered as super food due to high nutrient values. Due to these properties the pot-based project was designed to explore the most diverse accession of this species, 31 local S. oleracea accessions were evaluated taxonomically on the basis of morphological and anatomical markers. Seeds were sown in clay pots to evaluate different morphological characters. The germination period varied from 6 to 15 days while variation in number of leaves was observed to be from 12 to 68 leaves per plant. Moreover, abaxial and adaxial epidermal leaf analysis was examined under light microscope to study the existing anatomical variations. Substantial variations were observed among quantitative characters including number of subsidiary cells (3-5 on both abaxial and adaxial sides), stomata number per unit area (6-21 on abaxial side; 8-18 on adaxial side) and stomatal index (13.9-28.6 on abaxial side; 14.8-25.5 on adaxial side). The shape of epidermal cells varied from tetragonal to pentagonal, hexagonal and irregular. The results concluded that leaf epidermal anatomical markers could be applied considerably in delimiting the species to solve the existing taxonomic problems among them. However, for more authentic results it would be preferred to use more taxonomic tools in integration with anatomy to study intraspecific variations.

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S‐metolachlor in pre‐emergence reduces the leaf cuticular wax and increases the efficiency of glyphosate

Files,

Viana,

Camargo

et al. 2024

Pest Management Science

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BACKGROUNDThe low glyphosate uptake capacity, possibly due to the lipophilic character of epicuticular wax on leaves, may contribute to the natural tolerance of some weed species. The use of pre‐emergence herbicides like S‐metolachlor, which inhibits very‐long‐chain fatty acid synthesis, might enhance glyphosate's post‐emergent efficacy against hard‐to‐control weeds, such as Euphorbia heterophylla L. (milkweed) and Ipomoea triloba L. (littlebell). This study aimed to determine whether S‐metolachlor affects glyphosate's efficacy and its impact on epicuticular wax production in these species. In Experiment 1, milkweed was 2.27 times more tolerant to S‐metolachlor than littlebell, with GR50 values of 1024.6 g ai ha−1 and 451.2 g ai ha−1, respectively. In Experiment 2, S‐metolachlor enhanced glyphosate efficacy in both species. For littlebell, S‐metolachlor increased glyphosate efficacy by 28% at 21 days after treatment (DAT), while milkweed boosted control to 61%, compared to 45% in untreated plants. The herbicide accelerated glyphosate's action, particularly at lower doses. In Experiment 3, S‐metolachlor reduced epicuticular wax by up to 47% in littlebell and 45% in milkweed during early growth. Wax levels remained lower in treated plants, contributing to improved glyphosate efficacy.CONCLUSIONS‐metolachlor in pre‐emergence reduces the amount of wax deposited in the leaf surface, increasing glyphosate efficiency in both species, especially at the beginning of the phenological stage. © 2024 Society of Chemical Industry.

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Leaf and stem anatomical characterization of Euphorbia hirta L., a tolerant species to glyphosate

Cited by 3 publications

References 25 publications

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

Taxonomic Evaluation of Spinacia oleracea L. Accessions by Morphological and Anatomical Markers

S‐metolachlor in pre‐emergence reduces the leaf cuticular wax and increases the efficiency of glyphosate

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