无人机图像的无监督领域自适应和超分辨率用于牧草干生物量的自主估算
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
摘要：
牧草质量产量和组成估计是奶农确保为放牧和随后的牛奶生产提供充足的高质量牧草的重要工具。通过准确估计牧草质量和组成，可以部署有针对性的氮肥施用策略，以改善牧草地的局部区域，有效减少过度施肥对生物多样性和环境的负面影响。在这种情况下，深度学习算法提供了一种诱人的替代方法，以替代通常的草地成分估计方法，这种方法涉及从牧草地切割样本并手动分类牧草中所有植物物种的破坏性过程。这个过程是劳动密集型和耗时的，因此农民不使用。深度学习已成功应用于地面高分辨率相机收集的图像。然而，将深度学习解决方案移动到无人机成像，有可能通过将地面估计扩展到田地/围场占用的大面积来进一步改善牧草质量产量和成分估计任务。无人机图像的代价是从高空拍摄的田地分辨率较低，并且需要从无人机图像覆盖的大面积表面进一步收集牧草地面实况。本文提出以无监督的方式将地面图像上学到的知识转移到原始无人机图像。为此，我们使用不成对的图像风格转换将无人机图像的分辨率提高了8倍，并将其修改为更接近地面的图像。然后我们... ~ \url{www.github.com/PaulAlbert31/Clover_SSL}。
英文摘要：
Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then ... ~\url{www.github.com/PaulAlbert31/Clover_SSL}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
