fpls-11-00159.pdf (5.56 MB)

Assessment of mixed sward using context sensitive convolutional neural networks

Download (5.56 MB)
journal contribution
posted on 2023-05-03, 21:58 authored by Christopher Bateman, Jaco Fourie, Jeffrey Hsiao, Kenji Irie, Angus HeslopAngus Heslop, Anthony Hilditch, Steve GebbieSteve Gebbie, Kioumars Ghamkhar
Breedinghigheryieldingforagespeciesislimitedbycurrentmanualharvestingandvisualscoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) yield estimation in a mixed sward. We present a new convolutional neural network (CNN) architecture designed for semantic segmentation of dense pasture and canopies with high occlusion to which we have named the Local Context Network (LC-Net). On our testing data set we obtain a mean accuracy of 95.4% and a mean intersection over union of 81.3%, outperforming other methods we have found in the literature for segmenting clover from ryegrass. Comparing the clover/vegetation fraction for visual coverage and harvested dry-matter however showed little improvement from the segmentation accuracy gains. Further gains in biomass estimation accuracy may be achievable through combining RGB with complimentary information such as volumetric data from other sensors, which will form the basis of our future work


Rights statement

© 2020 Bateman, Fourie, Hsiao, Irie, Heslop, Hilditch, Hagedorn, Jessep, Gebbie and Ghamkhar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


  • English

Does this contain Māori information or data?

  • No


Frontiers Media

Journal title

Frontiers in Plant Science




Bateman, C. J., Fourie, J., Hsiao, J., Irie, K., Heslop, A., Hilditch, A., … Ghamkhar, K. (2020). Assessment of mixed sward using context sensitive convolutional neural networks. Frontiers in Plant Science, 11, 159. doi:10.3389/fpls.2020.00159

Contract number


Job code


Usage metrics


    Ref. manager