Bateman Christopher J, Fourie Jaco, Hsiao Jeffrey, Irie Kenji, Heslop Angus, Hilditch Anthony, Hagedorn Michael, Jessep Bruce, Gebbie Steve, Ghamkhar Kioumars
Lincoln Agritech Limited, Lincoln University, Lincoln, New Zealand.
Red Fern, Solutions Limited, Christchurch, New Zealand.
Front Plant Sci. 2020 Feb 27;11:159. doi: 10.3389/fpls.2020.00159. eCollection 2020.
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 ( L.) and perennial ryegrass ( 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.
目前用于测量或估算生物量的人工收割和视觉评分技术限制了高产牧草品种的培育。近年来，自动化和遥感高通量表型分析已成为解决这一瓶颈的可行方案。在此，我们重点研究利用RGB成像和深度学习来估算混播草地中白三叶草（L.）和多年生黑麦草（L.）的产量。我们提出了一种新的卷积神经网络（CNN）架构，专为密集牧场和高遮挡冠层的语义分割而设计，我们将其命名为局部上下文网络（LC-Net）。在我们的测试数据集上，我们获得了95.4%的平均准确率和81.3%的平均交并比，优于我们在文献中找到的其他从黑麦草中分割三叶草的方法。然而，比较视觉覆盖和收获干物质的三叶草/植被比例，分割精度的提高并没有带来显著改善。通过将RGB与其他传感器的体积数据等补充信息相结合，可能会进一步提高生物量估计的准确性，这将是我们未来工作的基础。
