Transesterification of mixtures of triglycerides in the presence of cotton-plant lipase

1983 ◽  
Vol 19 (4) ◽  
pp. 411-413 ◽  
Author(s):  
Z. Kh. Kadyrova ◽  
S. A. Abdurakhimov ◽  
K. K. Khalniyazov
Keyword(s):  
Cotton Plant ◽  
Plant Lipase

Effects of Some Insecticide Formulations on Fruiting of the Cotton Plant 1

Crop Science ◽  
1964 ◽  
Vol 4 (1) ◽  
pp. 97-98 ◽  
Author(s):  
Bruce Roark ◽  
T. R. Pfrimmer ◽  
M. E. Merkl
Keyword(s):  
Cotton Plant

scholarly journals Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo
Keyword(s):  
Deep Learning ◽  
Cotton Plant ◽  
Unmanned Aerial System ◽  
Learning Models ◽  
Training Images ◽  
Testing Dataset ◽  
Cotton Plants ◽  
Detection And Counting ◽  
Different Dimensions ◽  
The Mean

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.

A Microradioautographic Study of 14C-Diuron Absorption by Cotton

Weed Science ◽  
1971 ◽  
Vol 19 (4) ◽  
pp. 355-362 ◽  
Author(s):  
R. H. Strang ◽  
R. L. Rogers
Keyword(s):  
Gossypium Hirsutum ◽  
Cotton Plant ◽  
Freeze Drying ◽  
Effective Concentration ◽  
Transpiration Stream ◽  
Pigment Glands

The techniques of cryostat sectioning, freeze-drying, and microradioautography were adapted to study the absorption and translocation of radio-labeled 3-(3,4-dichlorophenyl)-1,1-dimethylurea (14C-diuron) in root-treated cotton (Gossypium hirsutum L., var. Coker 201) plants. Diuron appeared to move primarily in the apoplast and to be transported acropetally and laterally in the transpiration stream. Radioactivity was observed to accumulate in striking concentrations in the lysigenous, or pigment, glands and the trichomes of the cotton plant. This accumulation is postulated to be a major factor in lowering the effective concentration of the herbicide in the leaves of cotton as compared to leaves of more susceptible plants, and thus may be a significant factor in the tolerance of cotton to diuron.

scholarly journals Mineral Nutrition of the Cotton Plant.

1957 ◽  
Vol 32 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Frank M. Eaton ◽  
David R. Ergle
Keyword(s):  
Cotton Plant ◽  
Mineral Nutrition

Effect of the Cotton Plant in the Sudan Gezira on Certain Leaf-feeding Insect Pests

Nature ◽  
1958 ◽  
Vol 182 (4647) ◽  
pp. 1463-1464 ◽  
Author(s):  
R. J. V. JOYCE
Keyword(s):  
Cotton Plant ◽  
Insect Pests ◽  
Sudan Gezira
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