[Download] - Gibbs J. A., McAusland L., Zazueta C. R., Murchie E. H., Burgess A.J. (2021). A deep learning method for fully automatic stomatal morphometry and maximal conductance estimation. Frontiers in Plant Science, Technical Advances in Plant Science.
[Download] - Burgess A. J., Durand M., Gibbs J. A., Retkute R., Robson T. M., Murchie E. H. (2021). The effect of canopy architecture on the patterning of “windflecks” within a wheat canopy. Plant, Cell & Environment.
[Download] - Gibbs, J. A., Burgess, A. J., Pound, M. P., Pridmore, T. P., & Murchie, E. H. (2019). Recovering wind-induced plant motion in dense field environments via deep learning and multiple object tracking. Plant physiology, 181(1), 28-42.
[Download] - Burgess, A. J., Gibbs, J. A., & Murchie, E. H. (2019). A canopy conundrum: can wind-induced movement help to increase crop productivity by relieving photosynthetic limitations? Journal of Experimental Botany, 70(9), 2371-2380.
[Download] - Gibbs, J. A.., Pound, M., French, A., Wells, D., Murchie, E. H., & Pridmore, T. (2019). Active vision and surface reconstruction for 3D plant shoot modelling. IEEE/ACM Trans. Computational Biology and Bioinformatics. (99), 1.
[Download] - Gibbs, J. A.., Pound, M., French, A. P., Wells, D. M., Murchie, E. H., & Pridmore, T. (2018). Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction. Plant physiology, 178(2), 524-534.
[Download] - Gibbs, J. A., Pound, M., French, A. P., Wells, D. M., Murchie, E., & Pridmore, T. (2017). Approaches to three-dimensional reconstruction of plant shoot topology and geometry. Functional Plant Biology, 44(1), 62-75.
[Download] - Gibbs, J. A., Pound, M. P., Wells, D. M., Murchie, E. H., French, A. P., & Pridmore, T. P. (2015). Three-dimensional reconstruction of plant shoots from multiple images using an active vision system. Proceedings of the IROS Workshop on Agri-Food Robotics.
[Download] - Gibbs, J. A., Kendall, G., & Özcan, E. (2010, September). Scheduling English football fixtures over the holiday period using hyper-heuristics. In International Conference on Parallel Problem Solving from Nature (pp. 496-505). Springer, Berlin, Heidelberg.