• 한국작물학회지
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• 제47권1호
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• pp.13-20
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• 2002

Bread baking parameters and relationships between bread baking properties and flour characteristics were evaluated for two years, 1997 and 1998, and at two locations, Suwon and Deokso, with Korean winter wheat cultivars and lines. Among the bread baking parameters, lightness of crumb grain showed differences between years. No significant differences were found in dough mixing time, bread loaf volume, crumb grain score or firmness. Keumkangmil, Suwon 278 and Tapdongmil showed higher bread loaf volume, good structure of crumb grain and softer crumb firmness. However, compared to commercial flours for baking, cultivar means averaged over years and locations of nineteen Korean winter wheats showed poor bread baking quality because of low protein content and unsuitable protein quality. Protein content and flour swelling volume showed better relationships with the bread baking parameters than other flour characteristics. Friabilin-absence lines showed softer crumb firmness than those of friabilin-presence lines.

• 디지털콘텐츠학회 논문지
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• 제19권6호
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• pp.1197-1205
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• 2018

본 논문에서는 함정전투체계의 EOTS나 IRST에서 획득한 영상을 초고해상도 영상으로 복원한다. 저해상도에서 초고해상도의 영상을 생성하는 생성 모델과 이를 판별하는 판별 모델로 구성된 생성적 적대 신경망을 이용하고, 다양한 학습 파라미터의 변화를 통한 최적의 값을 제안한다. 실험에 사용되는 학습 파라미터는 crop size와 sub-pixel layer depth, 학습 이미지 종류로 구성되며, 평가는 일반적인 영상 품질 평가 지표에 추가적으로 특징점 추출 알고리즘을 함께 사용하였다. 그 결과, Crop size가 클수록, Sub-pixel layer depth가 깊을수록, 고해상도의 학습이미지를 사용할수록 더 좋은 품질의 영상을 생성한다.

• 한국작물학회지
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• 제63권2호
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• pp.158-163
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• 2018

Perilla (Perilla frutescens var.frutescens) is an annual plant of the Lamiaceae family, mainly grown for obtaining oil by press extraction after roasting the seeds. Oil yield is one of its important traits, but evaluating this yield is time-consuming, requires many seeds, and is hard to adjust to pedigrees in a breeding field. The objective of this study was to develop a method for selecting high-oil-yield lines in a breeding population without oil extraction. Twenty-three perilla cultivars were used for evaluating the oil yield and seed traits such as seed hardness, seed coat thickness, seed coat proportion and crude fat. After evaluation of the seed traits of 23 perilla cultivars, the ranges of oil yields, seed hardness, seed coat thickness, seed coat proportion, 100-seed weight, and crude fat were 24.68-38.75%, 157-1166 gf, $24-399{\mu}m$, 15.4-41.5%, 2.79-6.69 g, and 33.0-47.8%, respectively. In an analysis of correlation coefficients, the oil yield negatively correlated with seed length, seed width, the proportion of seed coat, seed hardness, and 1000-seed weight, but positively correlated with crude fat content. It was observed that as the seed coat proportion increased, the seed coat thickness, hardness, and 1000-seed weight also increased. Multiple linear regression (MLR) was employed to find major variables affecting the oil yield. Among the variables, traits crude fat content and seed coat proportion were assumed to be indirect parameters for estimating the potential oil yield, with respect to a significant positive correlation with the observed oil yield ($R^2=0.791$). Using these two parameters, an equation was derived to predict the oil yield. The results of this study show that various seed traits in 23 perilla cultivars positively or negatively correlated with the oil yield. In particular, crude fat and the seed coat proportion can be used for predicting the oil yield with the newly developed equation, and this approach will improve the efficiency of selecting prominent lines for the oil yield.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• 한국작물학회지
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• 제28권4호
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• pp.462-468
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• 1983

1980년 포장에 공시했던 38개 유색 대두종에 대한 관능검사 결과, 그리고 관능검사치와 Texturometer Parmeter의 수치 및 종실의 화학성분 함량상호간 상관성을 요약하면 다음과 같다. 1. 대부분의 식미성분은 종실중보다 품종별 특성에 영향을 받으며, 종실중$\times$품종 상호작용 효과에 따라서도 유의적인 차이를 나타내었다. 2. 38개 공시 수집종중 전체적인 식미 수용도가 높은 10개종을 선발할 수 있었다. 3. 관능검사치, Texturometer측정치 및 종실성분간에는 형질에 따라 유의한 정 또는 부의 상관계수가 인정되었다. 4. 전체적인 유색 대두의 Overall-Acceptance를 Texturometer 요인들의 다중회귀로 산출한 결과, Cohesiveness와 Gumminess의 회귀계수가 컸으며, 소ㆍ중립은 Cohesiveness가, 대ㆍ특대립은 Gumminess가 컸다. Sensory 요인 중에서 Overall-Acceptance에 가장 높은 회귀계수를 보이는 것은 식후감이었다. 또 Y축(Acceptance Degree)은 대립 > 특대립 > 중립 > 소립의 순으로 높았다. 5. Overall-Acceptance에 대한 기여도는 소립ㆍ중립ㆍ특대립의 경우에는 Cohesiveness가 가장 컸으나 대립에서는 Hardness였으며, 관능검사치 가운데서는 일반적으로는 물렁이지 않고 씹히는 맛이 있는 특성들의 기여도가 컸다.

• 한국약용작물학회지
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• 제26권6호
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• pp.447-454
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• 2018

Background: The leaf temperature ($T_{LEAF}$) is one of the most important physical parameters governing water and carbon flux, including evapotranspiration, photosynthesis and respiration. Cnidium officinale is one of the important folk medicines for counteracting a variety of diseases, and is particularly used as a traditional medicinal crop in the treatment of female genital inflammatory diseases. In this study, we developed a model to estimate $T_{Leaf}$ of Cnidium officinale Makino based on black globe temperature ($T_{BGT}$). Methods and Results: This study was performed from April to July 2018 in field characterized by a valley and alluvial fan topography. Databases of $T_{LEAF}$ were curated by infrared thermometry, along with meteorological instruments, including a thermometer, a pyranometer, and an anemometer. Linear regression analysis and Student's t-test were performed to evaluate the performance of the model and significance of the parameters. The correlation coefficient between observed $T_{LEAF}$ and calculated $T_{BGT}$ obtained using an equation, developed to predict $T_{LEAF}$ based on $T_{BGT}$ was very high ($r^2=0.9500$, p < 0.0001). There was a positive relationship between $T_{BGT}$ and solar radiation ($r^2=0.8556$, p < 0.0001), but a negative relationship between $T_{BGT}$ and wind speed ($r^2=0.9707$, p < 0.0001). These results imply that heat exchange in leaves seems to be mainly controlled by solar radiation and wind speed. The correlation coefficient between actual and estimated $T_{BGT}$ was 0.9710 (p < 0.0001). Conclusions: The developed model can be used to accurately estimate the $T_{Leaf}$ of Cnidium officinale Makino and has the potential to become a practical alternative to assessing cold and heat stress.

• 한국초지조사료학회지
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• 제39권4호
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• pp.292-297
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• 2019

본 연구는 국내산 사료용 벼를 수집하여 근적외선분광법을 이용한 신속한 품질평가를 위하여 2018년 조사료 품질분석 기관의뢰 된 시료 564점을 수집하여 품질평가 NIR-DB를 구축하고 구축된 DB를 바탕으로 최적의 품질평가 검량식을 개발하고 검증하였다. 각 성분별로 예측 정확성을 평가하기 위해 스펙트라를 측정한 값과 실험실 분석값 간의 상관관계를 이용한 다변량분석법을 이용하였다. 사료용 벼의 수분함량 평가에 대한 예측능력은 각각 SEC 1.66% (R²=0.99)와 SECV 1.81% (R²=0.98)로 나타나 사료가치 평가 성분 중 가장 우수한 예측 능력을 보였으며, CP 함량 각각 SEC 0.42% (R²=0.93)와 SECV 0.50% (R²=0.89)로 나타났다. ADF와 NDF 함량의 예측능력은 각각 SEC 1.25% (R²=0.84), SECV 1.42% (R²=0.79) 및 SEC 1.61% (R²=0.90), SECV 1.79%(R²=0.86)로 나타났다. 사료용 벼의 품질 등급인 RFV의 예측 능력은 SEC 4.67% (R²=0.88), SECV 5.21% (R²=0.84)로 나타났다. 이상의 결과를 종합해보면 근적외선분광법을 이용하여 국내산 사료용 벼의 수분함량과 각종 영양성분을 적은 오차범위에서 분석·평가가 가능하였다.

• 한국토양비료학회지
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• 제46권5호
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• pp.322-326
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• 2013

Many organic fruit growers adopt cover cropping in their orchards to improve soil properties. A field experiment was conducted to determine the effects of cutting frequency of cover crop (CF) and liquid manure application (LM) on green manure production (GMP) and returnable nutrient content (RNC) in pear orchard. The combined effects of CF and LM were tested at two levels, respectively, with liquid manure ($L_1$) and without liquid manure ($L_0$). After that, cover crops were cut once ($C_1$) and three times ($C_3$) in rye, and twice ($C_2$) and four times ($C_4$) in hairy vetch. The result showed that main factors related to green manure production were different depending on the species. In rye, LM was more effective in increasing the dry weight of cover crop and RNC than CF. In contrast, the parameters were more affected by CF rather than LM in hairy vetch. Thus, it is suggested that different management technique is needed depending on the cover crop species in order to maximize the green manure production in pear orchard.

• 농업생명과학연구
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• 제46권6호
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• pp.127-135
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• 2012

This study was conducted to investigate effects of dietary anthocyanin fortified barley (AFB) or whole crop barley (WCB) on growing performance, meat quality and blood properties in swine at late fattening phase. Swine performance was not significantly differed (p>0.05) but average daily gain and average daily feed intake tended to increase in treatments. Meat quality parameters including pH, cooking loss, shear force and meat surface color were not influenced by the addition of barley in diet. However, DPPH content of longgissmus dorsi muscle was significantly increased in WCB10 compared to control, AFB5 and WCB5 (p<0.05). FRAP content of longgissmus dorsi muslce was higher in WCB5 than the AFB (p<0.05), thereby, a tendency in FRAP was not similar to that of DPPH. Only myristic acid (C14:0) was affected, and the lowest myristic acid was found when AFB was supplied to swine. A tendency was not determined in total protein and HDL-cholesterol content, however, control, WCB10 and AFB5 had high in total protein and showed significantly low values in HDL-cholesterol. Therefore, the results indicate that barley can be considered as an ingredient in swine diet, but further investigation is necessary.