Korean Journal of Agricultural Science最新文献_第3页

Biological control of powdery mildew in Korean melons through a treatment with a culture of Bacillus species 芽孢杆菌培养对韩国甜瓜白粉病的生物防治

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200084

S. Lee, N. Jeon, Myung-soo Park, H. Yun

引用次数: 0

Effect of organic fertilizer mixed with dehydrated food waste powder on growth of leaf lettuce 有机肥与脱水厨余粉混施对生菜生长的影响

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200085

Jun-Hyuk Yoo, Jae-Hong Kim, Jaehan Lee, Jin-Hyuk Chun, Luyima Deogratius, Yun-Gu Kang, Hyun-Nyung Woo, T. Oh, S. Kim

引用次数: 1

Tillage boundary detection based on RGB imagery classification for an autonomous tractor 基于RGB图像分类的自动拖拉机耕作边界检测

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200006

Gook-Hwan Kim, Dasom Seo, Kyoung-Chul Kim, Youngki Hong, Meong-hun Lee, S. Lee, Hyunjong Kim, H. Ryu, Yong-Joo Kim, Sun-Ok Chung, Dae-Hyun Lee

{"title":"Tillage boundary detection based on RGB imagery classification for an autonomous tractor","authors":"Gook-Hwan Kim, Dasom Seo, Kyoung-Chul Kim, Youngki Hong, Meong-hun Lee, S. Lee, Hyunjong Kim, H. Ryu, Yong-Joo Kim, Sun-Ok Chung, Dae-Hyun Lee","doi":"10.7744/KJOAS.20200006","DOIUrl":"https://doi.org/10.7744/KJOAS.20200006","url":null,"abstract":"In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9o. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"22 1","pages":"205-217"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83420727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Evaluation of ammonia (NH 3 ) emissions from soil amended with rice hull biochar 稻壳生物炭改性土壤氨(nh3)排放评价

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200088

Seong-Yong Park, Hanna Choi, Yun-Gu Kang, Seong-Jin Park, D. Luyima, Jaehan Lee, T. Oh

引用次数: 6

Pentoxifylline treatment of frozen pig sperm affects sperm motility and fetal numbers 己酮茶碱处理冷冻猪精子会影响精子活力和胎儿数量

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200053

S. Baek, Chung Hak-Jae, J. Hong, E. Cho, I. Choi

引用次数: 1

Digital image-based plant phenotyping: a review 基于数字图像的植物表型分析综述

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200004

M. Omari, Jayoung Lee, M. A. Faqeerzada, Rahul Joshi, Eunsoo Park, B. Cho

{"title":"Digital image-based plant phenotyping: a review","authors":"M. Omari, Jayoung Lee, M. A. Faqeerzada, Rahul Joshi, Eunsoo Park, B. Cho","doi":"10.7744/KJOAS.20200004","DOIUrl":"https://doi.org/10.7744/KJOAS.20200004","url":null,"abstract":"With the current rapid growth and increase in the world’s population, the demand for nutritious food and fibers and fuel will increase. Therefore, there is a serious need for the use of breeding programs with the full potential to produce high-yielding crops. However, existing breeding techniques are unable to meet the demand criteria even though genotyping techniques have significantly progressed with the discovery of molecular markers and nextgeneration sequencing tools, and conventional phenotyping techniques lag behind. Wellorganized high-throughput plant phenotyping platforms have been established recently and developed in different parts of the world to address this problem. These platforms use several imaging techniques and technologies to acquire data for quantitative studies related to plant growth, yield, and adaptation to various types of abiotic or biotic stresses (drought, nutrient, disease, salinity, etc.). Phenotyping has become an impediment in genomics studies of plant breeding. In recent years, phenomics, an emerging domain that entails characterizing the full set of phenotypes in a given species, has appeared as a novel approach to enhance genomics data in breeding programs. Imaging techniques are of substantial importance in phenomics. In this study, the importance of current imaging technologies and their applications in plant phenotyping are reviewed, and their advantages and limitations in phenomics are highlighted.","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"28 1","pages":"119-130"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88178030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Properties of hydrolyzed α-lactalbumin, β-lactoglobulin and bovine serum albumin by the alcalase and its immunemodulation activity in Raw 264.7 cell alcalase水解α-乳白蛋白、β-乳球蛋白和牛血清白蛋白的性质及其在Raw 264.7细胞中的免疫调节活性

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200035

Jae Min Yu, J. Son, Gerelyuya Renchinkhand, Kwang-Yeon Kim, J. Sim, Nam, Myoung-soo

引用次数: 0

Sequential sampling method for monitoring potato tuber moths (Phthorimaea operculella) in potato fields 马铃薯田马铃薯块茎蛾(Phthorimaea operculella)监测的序贯抽样方法

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200049

Jae‐Min Jung, Dae‐hyeon Byeon, Eunji Kim, Hye-Min Byun, Jaekook Park, Ji-Hoon Kim, Baek Jongmin, Kyutae Kim, M. Roca‐Cusachs, Minjoon Kang, Subin Choi, Sumin Oh, Sunghoon Jung

引用次数: 0

Prediction of the DO concentration using the machine learning algorithm: case study in Oncheoncheon, Republic of Korea 使用机器学习算法预测DO浓度:在韩国Oncheoncheon的案例研究

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200086

Heesung Lim, H. An, E. Choi, Yeonsu Kim

引用次数: 2

Effect of dietary ractopamine supplementation on growth performance, meat quality and fecal score in finishing pigs 饲粮添加莱克多巴胺对育肥猪生长性能、肉品质和粪便评分的影响

Korean Journal of Agricultural Science Pub Date : 2020-01-01 DOI: 10.7744/KJOAS.20200098

M. Hoque, Yu-Mi Im, I. Kim

引用次数: 1