scholarly journals Studies on the Stomatal Movement of Tobacco Leaves : 1. The mevement of stomata in the field-grown tobacco by the time in a day and in the course of leaf maturity

1968 ◽  
Vol 37 (1) ◽  
pp. 107-111
Author(s):  
Mitsuyoshi SHODA ◽  
Shiro HIGASE
Keyword(s):  
Stomatal Movement ◽  
Tobacco Leaves ◽  
Leaf Maturity

scholarly journals Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Chen ◽  
Jun Bin ◽  
Congming Zou ◽  
Mengjiao Ding
Keyword(s):  
Deep Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Learning Ability ◽  
Support Vector ◽  
Maturity Level ◽  
Promising Alternative ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Leaf Maturity

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

INFLUENCE OF SPRINKLER IRRIGATION ON THE INCIDENCE OF WEATHER FLECK ON FLUE-CURED TOBACCO IN ONTARIO

1961 ◽  
Vol 41 (2) ◽  
pp. 281-287 ◽  
Author(s):  
E. K. Walker ◽  
L. S. Vickery
Keyword(s):  
Plant Growth ◽  
Irrigation Water ◽  
Sprinkler Irrigation ◽  
Tobacco Leaves ◽  
Leaf Maturity

Indices of amounts of weather fleck were evolved from field inspections of flue-cured tobacco subjected to variable amounts of irrigation water. The susceptibility of tobacco leaves to weather fleck increased with maturation, reaching a maximum when they became mature. Occurrence and intensity of the disorder were found to be directly related to amounts of incident water, which hastened leaf maturity and rate of plant growth.

An improved lightweight network architecture for identifying tobacco leaf maturity based on Deep learning

2021 ◽  
pp. 1-10
Author(s):  
JX Li ◽  
H Zhao ◽  
S.P Zhu ◽  
H. Huang ◽  
YJ Miao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Superior Performance ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Network Information ◽  
Leaf Maturity ◽  
Model Size

The classification of fresh tobacco leaves during the picking process plays an important role in the subsequent roasting. In this paper, a lightweight convolutional neural network is used to detect the maturity of tobacco leaves quickly. Fresh tobacco leaves in the datasets are divided into 3 categories by the picking position, and each category is divided into 4 maturity levels and finally gets 12 types of tobacco leaves with different maturity. To ensure the lightweight of the model, the new network is based on the MobileNetV2 to establish. By utilizing shortcut operation, the shallow network information is preserved, and network degradation is suppressed. In the tobacco leaf datasets we obtained, the improved network has superior performance and compared with other classic networks, the model size and the number of operations have been reduced.

The influence of growth conditions and leaf maturity in relation to the chlorogenic acid stimulation of glycollate oxidase from tobacco leaves

1974 ◽  
Vol 52 (1) ◽  
pp. 209-215 ◽  
Author(s):  
Donald W. De Jong
Keyword(s):  
Chlorogenic Acid ◽  
Direct Consequence ◽  
Hill Reaction ◽  
Acid Oxidase ◽  
Leaf Extracts ◽  
Tobacco Leaves ◽  
Leaf Maturity ◽  
Activity Measurements ◽  
Glycollate Oxidase

Glycollate oxidase (glycollate: oxygen oxidoreductase EC. 1.1.3.1) levels in tobacco leaves from plants grown under three different photoperiods and three different temperature regimes were compared. Leaf extracts obtained from mature plants grown at moderate temperatures (25–20C; day/night) and with intermediate day lengths (10 h) showed highest activity. In contrast, peroxidase (EC. 1.11.1.7) was highest in extracts from plants grown at low temperatures and short days while chlorogenic acid oxidase (EC. 1.10.3.1) was highest in leaf extracts from plants grown at high temperatures. Other investigations revealed that old leaves contained consistently lower glycollate oxidase levels than young leaves from the same plant. The addition of chlorogenic acid to the assay medium tended to equalize differences in enzyme activity. Photosynthetic efficiency as determined by O2 evolution (Hill reaction) and photophosphorylation in isolated chloroplasts improved with leaf maturity but this development did not appear to be a direct consequence of low photorespiratory activity. Measurements of the conversion of glycollate-1-14C to 14CO2 by leaf disks under light or dark conditions indicated that chlorogenic acid affects glycollate metabolism in vivo. It is postulated that chlorogenic acid moderates photorespiration by interacting with glycollate oxidase.

Low temperature x-ray microanalysis of frozen-hydrated tobacco leaves

1984 ◽  
Vol 42 ◽  
pp. 284-285
Author(s):  
S. Edith Taylor ◽  
Patrick Echlin ◽  
May McKoon ◽  
Thomas L. Hayes
Keyword(s):  
Low Temperature ◽  
Lemna Minor ◽  
Root Tips ◽  
Tobacco Leaves ◽  
X Ray ◽  
Whole Cells ◽  
Carbon Coated ◽  
The Right ◽  
Beam Currents ◽  
The University

Low temperature x-ray microanalysis (LTXM) of solid biological materials has been documented for Lemna minor L. root tips. This discussion will be limited to a demonstration of LTXM for measuring relative elemental distributions of P,S,Cl and K species within whole cells of tobacco leaves.Mature Wisconsin-38 tobacco was grown in the greenhouse at the University of California, Berkeley and picked daily from the mid-stalk position (leaf #9). The tissue was excised from the right of the mid rib and rapidly frozen in liquid nitrogen slush. It was then placed into an Amray biochamber and maintained at 103K. Fracture faces of the tissue were prepared and carbon-coated in the biochamber. The prepared sample was transferred from the biochamber to the Amray 1000A SEM equipped with a cold stage to maintain low temperatures at 103K. Analyses were performed using a tungsten source with accelerating voltages of 17.5 to 20 KV and beam currents from 1-2nA.

Crosstalk of NO with Ca2+ in Stomatal Movement in Vicia faba Guard Cells

2009 ◽  
Vol 35 (8) ◽  
pp. 1491-1499 ◽  
Author(s):  
Lin ZHANG ◽  
Xiang ZHAO ◽  
Ya-Jing WANG ◽  
Xiao ZHANG
Keyword(s):  
Vicia Faba ◽  
Guard Cells ◽  
Stomatal Movement

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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