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威尼斯城娱乐官方平台» 学术动态» 关于举办“Machine Learning and Computer Vision applications in Proximal Soil Sensing” 学术报告的通知
 

关于举办“Machine Learning and Computer Vision applications in Proximal Soil Sensing” 学术报告的通知

来源:       发布日期:2019-12-06     浏览次数:

     

报告题目:Machine Learning and Computer Vision applications in Proximal Soil Sensing

报 告 人:Asim Biswas博士

报告时间:2019年12月9日(星期一)下午14:30-15:30

报告地点:资源环境学院307会议室

邀 请 人: 何海龙 副教授

报告摘要:Characterization and quantification of soil properties are important for the optimum use and management of our soil. Traditional methods for estimating soil properties are time consuming and laborious. In contrast, recent technological developments around proximal soil sensing has been showing great promise to meet the high-resolution spatial and temporal data demand for modern-day precision agriculture. More recently, with the advancements and developments in imaging techniques and computational powers of modern computer and handheld devices to process high resolution images have been gathering interest to characterize soil properties. Often the color and the surface textural characteristics of an image and image pixels is nothing but the presentation of the characteristics of that soil. Developing a relationship between the colors and the image surface textural properties as derived from an image with laboratory-measured soil properties show strong promise of image-based soil characterization. Image processing and various machine learning and computer vision algorithms provide the basis for developing these relationships. This paper brings together some examples from current studies on imaging soil in laboratory and in field conditions using various devices including handheld microscope, cell phone camera and digital camera and various image processing techniques including geostatistical, artificial neural network, support vector machine, wavelet transform to characterize soil texture and organic matter. Design and development of image acquisition systems, collection of soil images, processing and extraction of image parameters and development of models will provide information on the use of machine learning and computer vision applications to develop new proximal soil sensors.

报告人简介:

:Asim Biswas博士曾先后在加拿大McGill University和University of Guelph任职助理教授和副教授。主要研究领域为农业水文学,在包气带水文学、精准农业、土壤水分、热及溶质迁移和空间变异性等方面的研究非常深入,为量化研究土壤水文的相关属性和机制奠定了基础。Asim Biswas博士已发表SCI论文67篇,会议摘要131篇,参编著作1部,专利1项。目前主持科研项目十余项,其中主持项目的经费136万美元;在国际会议和研讨会上邀请发言28次,口头报告84次,海报17幅。指导和合作指导研究生15名。先后获得多向奖励,包括2010年获得加拿大土壤科学学会(CSSS)Bentley 奖;2010-2012年连续获得SSSA年会最佳海报奖,2011年度加拿大地球物理联盟(CGU) 最佳学生论文奖; 2012年University of Saskatchewan优秀博士论文奖;2012年Kirkham 旅游奖;2014年获美国地球物理联盟(AGU)颁发的Donald L. Turcotte奖;2016年及2018年两次获美国土壤科学学会土壤物理与水文青年科学家奖;2018年获美国土壤科学学会S6分会青年学者奖;2018年获安大略省研究、创新和科学部早期研究员奖等。

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资源环境学院

2019年12月6日


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