Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks
| Autor: | Bruce Jessep, Bateman Christopher James, Jeffrey Hsiao, Kenji Irie, Kioumars Ghamkhar, Angus Heslop, Michael Hagedorn, Anthony Hilditch, Steve Gebbie, Jaco Fourie |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
ryegrass Computer science forage yield Context (language use) Plant Science lcsh:Plant culture 01 natural sciences Lolium perenne Convolutional neural network Bottleneck Segmentation lcsh:SB1-1110 Throughput (business) Original Research biology biomass business.industry Deep learning deep learning Pattern recognition 04 agricultural and veterinary sciences clover biology.organism_classification semantic segmentation 040103 agronomy & agriculture Trifolium repens 0401 agriculture forestry and fisheries Artificial intelligence business 010606 plant biology & botany |
| Zdroj: | Frontiers in Plant Science, Vol 11 (2020) Frontiers in Plant Science |
| DOI: | 10.3389/fpls.2020.00159/full |
| Popis: | Breeding higher yielding forage species is limited by current manual harvesting and visual scoring 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. |
| Databáze: | OpenAIRE |
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