Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

Pound, Michael P.; Burgess, Alexandra J.; Wilson, Michael H.; Atkinson, Jonathan A.; Griffiths, Marcus; Jackson, Aaron S.; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M.; Murchie, Erik H.; Pridmore, Tony P.; French, Andrew P.

All Outputs (2)

Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies (2018)
Working Paper
Retkute, R., Townsend, A. J., Murchie, E. H., Jensen, O. E., & Preston, S. P. Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies

Background and Aims Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the comp... Read More about Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies.

Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping (2016)
Working Paper
Pound, M. P., Burgess, A. J., Wilson, M. H., Atkinson, J. A., Griffiths, M., Jackson, A. S., …French, A. P. Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-th... Read More about Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping.