Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating bree...Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs.A common challenge when capturing images in the field relates to the spectral reflection of sunlight(glare)from crop leaves that,at certain solar incidences and sensor viewing angles,presents unwanted signals.The research presented here involves the convergence of 2 parallel projects to develop a facile algorithm that can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf's tissue.展开更多
基金supported by Division of Electrical,Communications and Cyber Systems(1809753)National Institute of Food and Agriculture(2020-67021-31961).
文摘Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs.A common challenge when capturing images in the field relates to the spectral reflection of sunlight(glare)from crop leaves that,at certain solar incidences and sensor viewing angles,presents unwanted signals.The research presented here involves the convergence of 2 parallel projects to develop a facile algorithm that can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf's tissue.
