• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• 한국식품영양학회지
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• 제36권5호
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• pp.387-394
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• 2023

With the advancement and diversification of the bread industry, eco-friendly products with less sugar and salt, and containing functional ingredients are being developed. To develop healthy bread, Korean pine leaf powder was added in different proportions (0%, 1%, 3%, 5%, and 7%), and the quality characteristics of the bread, namely height, moisture, color value, texture, antioxidant property, and sensory characteristics were evaluated. As the amount of leaf powder was increased in the bread, L-value in the range of 53.45~85.05 (p<0.001) and adhesiveness in the range of 0.13~0.32 mJ (p<0.001) decreased significantly, whereas b-value in the range of 16.75~30.74 (p<0.001), total polyphenol content in the range of 466.83~669.13 ug/mL, ABTS- in the range of 0.46~43.23%, DPPH-radical in the range of 1.39~45.76%, scavenging capacities (p<0.001), color in the range of 3.27~5.40 (p=0.017) and texture in the range of 4.33~4.80 (p=0.006) preferences increased significantly. This study could increase the utilization of Korean pine leaf and the production of healthy food with antioxidant properties.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2001년도 추계정기 학술발표회 초록집
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• pp.104-105
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• 2001

• 현장농수산연구지
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• 제9권1호
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• pp.133-142
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• 2007

• Animal cells and systems
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• 제3권2호
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• pp.143-147
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• 1999

Annual production and redistribution of leaf litter were compared among three distinct understory patches in a temperate hardwood forest dominated by Quercus mongolica, Kalopanax pictus, Acer pseudo-sieboldianum, and Carpinus cordata. Two patches were located on a southwest-facing slope: one with an understory dominated by herbaceous plants (Patch S), and the other covered with evergreen dwarf bamboo, Sasa borealis (patch SS). The third patch was on the opposite slope with an understory dominated by herbaceous plants (Patch N). Annual leaf litterfall was averaged 330 g m$^{-2} yr$^{-2}$ in the three patches from 1994 to 1998. From mid-September 1996 to mid-September 1997, net transport of leaf litter over patch bound-aries was 1,824g m$^{-1}$ from Patch S to SS, 1,465g m$^{-1}$ from Patch S to N, and 886 g m$^{-1}$ from Patch SS to N. The amounts moving downslope out of Patch S, SS, and N were 2,548, 471, and 588g m$^{-1}$, respectively. When a mass balance approach was employed for the data of leaf litter transport, the results were relatively consistent with 216, 631, and 724g m$^{-2}$ of leaf litter stores in Patch S, SS, and N, respectively, in April 1997. This study suggests that leaf litter redistribution is largely regulated by aspect and understory type and exerts a significant effect on carbon processes in the forest ecosystem.

• Journal of Biosystems Engineering
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• 제25권4호
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• pp.293-300
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• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.

• 한국컴퓨터정보학회논문지
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• 제27권1호
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• pp.175-181
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• 2022

본 연구에서는 브로콜리 잎 헥산 분획물의 항염 효과를 평가하여 기능성 식품 및 화장품 소재로의 적용 가능성을 확인하였다. LPS-자극된 RAW264.7 세포에서 전염증성 사이토카인의 생성, iNOS와 COX-2의 발현, MAPK (ERK, JNK, p38) 및 브로콜리 잎 헥산 분획을 사용한 NF-κB의 인산화를 분석하였다. 브로콜리 잎 헥산 분획은 TNF-α, IL-4, IL-6, IL-1β 등의 전염증성 사이토카인의 분비와 iNOS와 COX-2의 발현을 억제했습니다. 또한, 브로콜리 잎 헥산 분획물은 MAPK와 NFκB의 인산화를 감소시켰다. 따라서 브로콜리 잎 헥산 분획물은 식품 및 화장품에서 천연 항염증 소재로 적용 가능성이 있는 것으로 판단된다. 향후 항염증 기전 및 주요 생리활성 물질의 규명에 대한 연구가 필요할 것으로 생각된다.

• Asian-Australasian Journal of Animal Sciences
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• 제18권7호
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• pp.966-972
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• 2005

A study to compare the effects of supplementing Delonix elata, Grewia similis, Tamarindus indica and sunflower seed cake on intake and growth rate of dual-purpose goats fed low quality Buffel grass (Cenchrus ciliaris) hay was carried out. Twenty-eight male goats aged five to seven months (mean weight 12.93${\pm}$3.94 kg) were randomly allocated to four dietary groups in a completely randomised design. The diets were hay plus Grewia similis, hay plus Delonix elata, hay plus Tamarindus indica and hay plus sunflower seed cake. All diets were supplemented with maize bran. The experimental period was 90 days. Voluntary dry matter intake of the supplements was higher for Tamarindus indica (275.5 g/day) and Grewia similis (201.8 g/day) and lowest for sunflower seed cake (81g/day). Goats supplemented with Grewia similis had the highest hay intake (183.8 g/day) while those supplemented with sunflower seed cake had the lowest hay intake (98.9 g/day). Animals fed browse supplements gained significantly more weight (p<0.001) than those with sunflower seed cake. There were no significant differences in live weight change between goats fed the different browses. However, those fed Tamarindus indica gained an average of 20.79 g/d which was slightly higher than the gains for those on Grewia similis and Delonix elata while those fed sunflower seed cake lost weight. Correspondingly, goats supplemented with browse leaf meals had higher feed conversion ratios than those supplemented with sunflower seed cake and required 23.91 to 35.06 g DM of feed to produce one g of weight gain per day. In a separate study, the DM disappearance pattern indicated that Grewia similis and Delonix elata were highly degradable compared to Tamarindus indica. At 24 h of incubation, DM degradability was 627, 588 and 345 g/kg DM for Grewia similis, Delonix elata and Tamarindus indica, respectively. In another study in vivo DM digestibility ranged from 46.1% (for hay alone) to 56.2% (for hay plus Grewia similis). It was concluded that the addition of Tamarindus indica, Grewia similis and Delonix elata leaf meals to Cenchrus ciliaris hay resulted in increased total DM intake, in vivo digestibility and growth rate. Therefore, leaf meals of indigenous browses particularly Tamarindus indica and Grewia similis could be used as supplementary feeds for small ruminants grazing on poor quality roughages during the dry season rather than use of expensive, less effective and intermittently available sunflower seed cake.

• Journal of Forest and Environmental Science
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• 제27권3호
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• pp.143-149
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• 2011

The possibility of employing biomass of Salix viminalis cv. Q683 as a resource of bio-energy was evaluated. The chemical analysis of S. viminalis cv. Q683 leaf biomass showed components such as, extractives (2.57%), lignin (39.06%), hemicellulose (21.61%), and cellulose (37.83%), whereas, its stem was composed of extractives (1.67%), lignin (23.54%), hemicellulose (33.64%), and cellulose (42.03%). The biomass of S. viminalis cv. Q683 was saccharified using two enzymes celluclast and viscozyme. The saccharification of S. viminalis cv. Q683 biomass was influenced by enzymes and their strengths. The optimal enzyme combination was found to be celluclast (59 FPU/g substrate) and viscozyme (24 FBG/g substrate). On saccharification the glucose from leaf and stem biomass was 7.5g/L and 11.7g/L, respectively after 72 hr of enzyme treatment. The biomass and enzyme-treated biomass served as the feedstock for ethanol production by fermentation. The ethanol production from stem and leaf biomass was 5.8 g/L and 2.2 g/L respectively, while the fermentation of the enzymatic hydrolysates yielded 5 g/L to 8 g/L bioethanol in 72 hours.