{"title":"Biological control of powdery mildew in Korean melons through a treatment with a culture of Bacillus species","authors":"S. Lee, N. Jeon, Myung-soo Park, H. Yun","doi":"10.7744/KJOAS.20200084","DOIUrl":"https://doi.org/10.7744/KJOAS.20200084","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"17 1","pages":"1011-1020"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75242478","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}
Jun-Hyuk Yoo, Jae-Hong Kim, Jaehan Lee, Jin-Hyuk Chun, Luyima Deogratius, Yun-Gu Kang, Hyun-Nyung Woo, T. Oh, S. Kim
{"title":"Effect of organic fertilizer mixed with dehydrated food waste powder on growth of leaf lettuce","authors":"Jun-Hyuk Yoo, Jae-Hong Kim, Jaehan Lee, Jin-Hyuk Chun, Luyima Deogratius, Yun-Gu Kang, Hyun-Nyung Woo, T. Oh, S. Kim","doi":"10.7744/KJOAS.20200085","DOIUrl":"https://doi.org/10.7744/KJOAS.20200085","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"18 1","pages":"1021-1027"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73422063","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}
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
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.
{"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}
Seong-Yong Park, Hanna Choi, Yun-Gu Kang, Seong-Jin Park, D. Luyima, Jaehan Lee, T. Oh
{"title":"Evaluation of ammonia (NH 3 ) emissions from soil amended with rice hull biochar","authors":"Seong-Yong Park, Hanna Choi, Yun-Gu Kang, Seong-Jin Park, D. Luyima, Jaehan Lee, T. Oh","doi":"10.7744/KJOAS.20200088","DOIUrl":"https://doi.org/10.7744/KJOAS.20200088","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"36 1","pages":"1049-1056"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86730740","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}
{"title":"Pentoxifylline treatment of frozen pig sperm affects sperm motility and fetal numbers","authors":"S. Baek, Chung Hak-Jae, J. Hong, E. Cho, I. Choi","doi":"10.7744/KJOAS.20200053","DOIUrl":"https://doi.org/10.7744/KJOAS.20200053","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"417 1","pages":"657-665"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78153875","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}
M. Omari, Jayoung Lee, M. A. Faqeerzada, Rahul Joshi, Eunsoo Park, B. Cho
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.
{"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}
Jae Min Yu, J. Son, Gerelyuya Renchinkhand, Kwang-Yeon Kim, J. Sim, Nam, Myoung-soo
{"title":"Properties of hydrolyzed α-lactalbumin, β-lactoglobulin and bovine serum albumin by the alcalase and its immunemodulation activity in Raw 264.7 cell","authors":"Jae Min Yu, J. Son, Gerelyuya Renchinkhand, Kwang-Yeon Kim, J. Sim, Nam, Myoung-soo","doi":"10.7744/KJOAS.20200035","DOIUrl":"https://doi.org/10.7744/KJOAS.20200035","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"49 1","pages":"459-470"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77955906","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}
{"title":"Prediction of the DO concentration using the machine learning algorithm: case study in Oncheoncheon, Republic of Korea","authors":"Heesung Lim, H. An, E. Choi, Yeonsu Kim","doi":"10.7744/KJOAS.20200086","DOIUrl":"https://doi.org/10.7744/KJOAS.20200086","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"20 1","pages":"1029-1037"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85328602","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}
{"title":"Effect of dietary ractopamine supplementation on growth performance, meat quality and fecal score in finishing pigs","authors":"M. Hoque, Yu-Mi Im, I. Kim","doi":"10.7744/KJOAS.20200098","DOIUrl":"https://doi.org/10.7744/KJOAS.20200098","url":null,"abstract":"","PeriodicalId":17916,"journal":{"name":"Korean Journal of Agricultural Science","volume":"47 1","pages":"707-715"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86448284","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}
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