Indirect assessment of callus fresh weight by non-destructive methods

Mottley, J.; Keen, B.

doi:10.1007/bf00269569

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…These calluses were subcultured every 3 weeks. The callus growth was measured by the Mottley's method (Mottley & Keen 1987). Cell suspensions were initiated from 5 weeks old friable calluses maintained on MSRT plus NAA (5.4 ~tM) and 2,4-D (4.5 ~tM).…”

Section: Dedifferentiated Culturesmentioning

confidence: 99%

Artemisia annua L.: dedifferentiated and differentiated cultures

Paniego

Giulietti

1994

Plant Cell Tiss Organ Cult

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Dedifferentiated and differentiated tissue cultures ofArtemisia annua L. for artemisinin production were carried out. The calluses were initiated on MS medium supplemented with sucrose (30 g 1-1), myoinositol (100 mg 1-1) and RT vitamins. The auxins used were naphtaleneacetic acid (NAA), indoleacetic acid (IAA), indolebutyric acid (IBA) and 2,4-dichlorophenoxyacetic acid (2,4--D). These were added to the basal medium either singly or in combination. The best results were obtained with 2.4-D (4.5 ~tM : ~t0.02 d-l ) and NAA (5.4 ~tM : ~t 0.06 d-1 ). Cell suspensions were established on the same media without agar. Suspension cultures showed different morphological characteristics according to the plant growth regulator supplied. Organized cultures were initiated from callus obtained on 2,4-D (4.5 ~tM) and from bud cultures. Medium containing 6-benzylaminepurine (BA) (8.9 ~tM) + NAA (0.54 ~tM); Zeatin (45.62 ~tM) + NAA (5.37 ~tM) or BA (8.9 ~tM) stimulated both organogenesis in callus (frequency of induction = 50%) and semi-organized tissue in shoot buds. BA (13.32 ~tM) + NAA (1.08 ~tM) or BA (13.32 ~tM) only stimulated multiple shoot cultures (frequency of induction = 80%). Regarding artemisinin content, while the values obtained were 1.13 and 0.78 mg gDW-1 in primary callus, artemisinin was not detected in cell suspension and only traces of it were found in multiple shoot cultures.

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Section: Dedifferentiated Culturesmentioning

confidence: 99%

Artemisia annua L.: dedifferentiated and differentiated cultures

Paniego

Giulietti

1994

Plant Cell Tiss Organ Cult

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“…1). Although the relationship between the two-dimensional projected area of the tissue and fresh weight was not determined here, image analysis has been previously used to determine fresh weight of callus (Mottley and Keen 1987;Hirvonen and Ojamo 1988), cell growth (Olofsdotter 1993;Ibaraki and Kurata 1997) and in vitro plant growth (Smith and Spomer 1987).…”

Section: Resultsmentioning

confidence: 99%

Isoxaflutole treatment leads to reversible tissue bleaching and allows for more effective detection of GFP in transgenic soybean tissues

Chiera

Ling

et al. 2008

In Vitro Cell.Dev.Biol.-Plant

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The effects of the herbicide isoxaflutole (IFT) on tissue growth and the detection of green fluorescent protein (GFP) in transgenic embryogenic soybean tissues were evaluated using image analysis. The inclusion of this "bleaching" herbicide at 3 or 10 mg l −1 in a standard soybean embryo proliferation medium resulted in a change in tissue color from green to non-pigmented over the course of a 4-wk experiment. Although the loss in pigmentation was observed in transgenic and non-transformed control tissues, tissue growth remained unaffected. GFP expression in three different transgenic soybean clones, representing low to moderate GFP expression levels, was easily detected and quantified using image analysis following culture of the tissues on an IFT-containing medium. Quantification of GFP in tissues from the same clones cultured in the absence of IFT, however, was difficult using image analysis. After transfer of transgenic embryogenic tissue from a medium containing IFT to a medium without IFT, the growth of pigment-containing tissue resumed. The bleaching effects from this herbicide appear to be reversible and make IFT and possibly other bleaching herbicides useful in the analysis of GFP expression in tissues, where interference from chlorophyll is problematic.

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“…It is therefore difficult to extract weeds from complex backgrounds [19,20]. At the same time, the phenotypic information for each plant is extracted as a predictive factor (e.g., measured value [21], leaf area index [22], pixel number [23]) to establish a single linear regression relationship with the fresh weight of the plant; this single-factor approach does not include sufficient information. The fresh weight of weeds is determined not by a single characteristic parameter but by a combination of multiple characteristics.…”

Section: Introductionmentioning

confidence: 99%

Two-Stream Dense Feature Fusion Network Based on RGB-D Data for the Real-Time Prediction of Weed Aboveground Fresh Weight in a Field Environment

Quan

et al. 2021

Remote Sensing

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The aboveground fresh weight of weeds is an important indicator that reflects their biomass and physiological activity and directly affects the criteria for determining the amount of herbicides to apply. In precision agriculture, the development of models that can accurately locate weeds and predict their fresh weight can provide visual support for accurate, variable herbicide application in real time. In this work, we develop a two-stream dense feature fusion convolutional network model based on RGB-D data for the real-time prediction of the fresh weight of weeds. A data collection method is developed for the compilation and production of RGB-D data sets. The acquired images undergo data enhancement, and a depth transformation data enhancement method suitable for depth data is proposed. The main idea behind the approach in this study is to use the YOLO-V4 model to locate weeds and use the two-stream dense feature fusion network to predict their aboveground fresh weight. In the two-stream dense feature fusion network, DenseNet and NiN methods are used to construct a Dense-NiN-Block structure for deep feature extraction and fusion. The Dense-NiN-Block module was embedded in five convolutional neural networks for comparison, and the best results were achieved with DenseNet201. The test results show that the predictive ability of the convolutional network using RGB-D as the input is better than that of the network using RGB as the input without the Dense-NiN-Block module. The mAP of the proposed network is 75.34% (IoU value of 0.5), the IoU is 86.36%, the detection speed of the fastest model with a RTX2080Ti NVIDIA graphics card is 17.8 fps, and the average relative error is approximately 4%. The model proposed in this paper can provide visual technical support for precise, variable herbicide application. The model can also provide a reference method for the non-destructive prediction of crop fresh weight in the field and can contribute to crop breeding and genetic improvement.

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Indirect assessment of callus fresh weight by non-destructive methods

Cited by 8 publications

References 12 publications

Artemisia annua L.: dedifferentiated and differentiated cultures

Artemisia annua L.: dedifferentiated and differentiated cultures

Isoxaflutole treatment leads to reversible tissue bleaching and allows for more effective detection of GFP in transgenic soybean tissues

Two-Stream Dense Feature Fusion Network Based on RGB-D Data for the Real-Time Prediction of Weed Aboveground Fresh Weight in a Field Environment

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