Plant phenotyping technologies have been developing rapidly over the last 2 decades. Plant sensors are becoming more precise, faster, easier to use and with lower cost. However, there are still several bottleneck issues in plant sensing, including the changing environmental conditions, the great variances on the plant, and the complicated GxTxEinteractions. These issues keep phenotyping difficult and limit further application of the sensor technologies. The Purdue plant sensor developers have been working innovatively to address these issues and develop the next generation plant sensing technologies. In this seminar, Dr. Jin will firstly introduce the 4 newly constructed phenotyping facilities at Purdue, including why they were built and how they’ve been used. He will then share his opinions on the current major challenges in plant sensing. Finally, Dr. Jin will talk about his most recent sensor solutions, including several new sensor products, new phenotyping models, and how AI helps in plant sensing.
Jian Jin received his Ph.D. in Agricultural Engineering from Iowa State University in 2009. He earned his M.S. degree in Computer Engineering from Denmark Technical University in 2005 and his B.S. degree in Computer Science from Zhejiang University in 2003.Prior to joining Purdue, he conducted research at DuPont Pioneer (now Corteva), where he was most recently a Technology Leader working on automatic phenotyping and sensing and led a team for the company’s hyperspectral imaging systems for automated plant screening.Dr. Jin’s major research interest at Purdue is to build the next generation automatic crop plant phenotyping systems, along with machine vision, data processing, statistics, and big data modeling. He also has interests in other areas of agricultural sensing, broadly defined, and in automation and robotics in agriculture.
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