Effect of Different Foliar Silicon Sources on Cotton Plants

Author(s):  
Jonas Pereira de Souza Junior ◽  
Renato de Mello Prado ◽  
Mariana Bomfim Soares ◽  
José Lucas Farias da Silva ◽  
Victor Hugo de Farias Guedes ◽  
...  
2018 ◽  
Vol 51 (1) ◽  
Author(s):  
Sohail Akhtar ◽  
Muhammad Nouman Tahir ◽  
Imran Amin ◽  
Rana Binyamin ◽  
Shahid Mansoor

2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.


2020 ◽  
Vol 164 ◽  
pp. 06015
Author(s):  
Kseniia Illarionova ◽  
Sergey Grigoryev

The aim of research was to characterize epiphyte micromycetes observed on variable cotton fibers accessions, to estimate the range of fiber destruction and select cotton, which were the most resistant to fungus damage. The accessions of differently colored Upland Cotton varieties (Gossypium hirsutum L.) evaluated: eleven cotton of natural green, twelve – of brown and eleven of conventional white color. Cotton plants have been grown in Sothern Federal District, RF. The fiber samples for the study were placed into a thermostat in sterile Petri dishes on moistened filter paper in order to stimulate the development of mycelium or sporulation of fungi naturally occurred on fibers. Incubation carried out in a thermostat at a +24-28 °C, humidity of 90-100% and exposed for 28 days. The samples examined with a microscope or binocular magnifier. Aspergillus ustus (Bainier), A. fumigatus Fresen., A. niger v. Tiegh., A. flavus Link, Penicillium aurantiogriseum Dierckx, P. notatum Westling, Rhizopus nigricans Ehrenb. and Alternaria alternata (Fuier) Keissler were detected. Compared with exposed white, accession of green and brown colors were significantly resistant to fungus. The mean of destruction (K) of white cotton varied up to 0.95, but colored accessions not exceeded 0.3 (initial destruction of the surface, not affecting internal fiber’s structure).


1968 ◽  
Vol 57 (4) ◽  
pp. 553-558
Author(s):  
R. L. Ridgway ◽  
L. A. Bariola ◽  
S. L. Jones ◽  
W. L. Lowry

Laboratory and field-cage studies were conducted in Texas in 1965 to evaluate treatments of the systemic insecticides, Azodrin (3-hydroxy-N-methyl-cis-crotonamide dimethyl phosphate), Bidrin (3-hydroxy-N, N dimethyl-cis-crotonamide dimethyl phosphate), American Cyanamid CL-47031 (cyclic ethylene (diethoxy-phosphinyl) dithioimidocarbonate) and Temik (2-methyl-2-(methylthio) propion-aldehyde O-(methylcarbamoyl) oxime), applied incorporated in lanolin to the stems of cotton plants against Heliothis zea (Boddie) and H. virescens (F.). Reductions in numbers of developing larvae of H. zea were substantial on individual plants the stems of which had been treated with Azodrin or CL-47031 and which were artificially infested with eggs. When first-instar larvae of H. zea or H. virescens were caged on plants 3, 7 or 14 days after stem treatment with 2.5, 5.0 or 100 mg. Azodrin, Bidrin or CL-47031 per plant, net mortalities ranged from 21 to 80 per cent after three days. The mortality of adults of H. zea provided with sucrose solutions containing 1 p.p.m. of the systemic insecticides indicated that Azodrin and Bidrin were about equally toxic and much more so than CL-47031 and Temik, and that of adults caged on individual plants in flower that had been treated with Azodrin or CL-47031 suggested that the moths may be killed by the systemic action of these insecticides translocated to the nectar. When adults of H. virescens were released on plants each treated with Azodrin at 25 or 30 mg. in large field cages, reductions in the numbers of eggs deposited, attributed to the effect on the moths of the insecticide in the nectar, and in the numbers of developing larvae, were substantial. Azodrin was the most consistently effective of the four insecticides evaluated.


2003 ◽  
Author(s):  
Chunqiong Yuan ◽  
Qingyu Guo ◽  
A Aniwiar ◽  
Xiaoling Pan

2014 ◽  
Vol 33 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Guoxin Shen ◽  
Jia Wei ◽  
Xiaoyun Qiu ◽  
Rongbin Hu ◽  
Sundaram Kuppu ◽  
...  

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