This work introduces a method that combines remote sensing and deep learning into a framework that is tailored for accurate, reliable and efficient counting and sizing of plants in aerial images. The investigated task focuses on two low-density crops, potato and lettuce. This double objective of counting and sizing is achieved through the detection and segmentation of individual plants by fine-tuning an existing deep learning architecture called Mask R-CNN. This paper includes a thorough discussion on the optimal parametrisation to adapt the Mask R-CNN architecture to this novel task. As we examine the correlation of the Mask R-CNN performance to the annotation volume and granularity (coarse or refined) of remotely sensed images of plants, we conclude that transfer learning can be effectively used to reduce the required amount of labelled data. Indeed, a previously trained Mask R-CNN on a low-density crop can improve performances after training on new crops. Once trained for a given crop, the Mask R-CNN solution is shown to outperform a manually-tuned computer vision algorithm. Model performances are assessed using intuitive metrics such as Mean Average Precision (mAP) from Intersection over Union (IoU) of the masks for individual plant segmentation and Multiple Object Tracking Accuracy (MOTA) for detection. The presented model reaches an mAP of $0.418$ for potato plants and $0.660$ for lettuces for the individual plant segmentation task. In detection, we obtain a MOTA of $0.781$ for potato plants and $0.918$ for lettuces.

中文翻译：

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用于无人机图像中植物计数和大小调整的Mask R-CNN改装策略

这项工作介绍了一种将遥感和深度学习结合到一个框架中的方法，该框架专门用于对航空影像中的植物进行准确，可靠和高效的计数和调整大小。被调查的任务集中于两种低密度作物，马铃薯和生菜。通过微调现有的称为Mask R-CNN的深度学习体系结构，对单个植物进行检测和分割，可以实现计数和大小确定的双重目标。本文包括对最佳参数设置的全面讨论，以使Mask R-CNN体系结构适应这一新颖任务。当我们检查Mask R-CNN性能与植物遥感图像的注释量和粒度（粗略或精细）的相关性时，我们得出结论，可以有效地使用转移学习来减少所需的标记数据量。确实，在低密度作物上进行过先前培训的Mask R-CNN可以提高对新作物进行培训后的性能。一旦针对给定的作物进行了训练，那么Mask R-CNN解决方案的性能将优于手动调整的计算机视觉算法。模型性能是使用直观的指标进行评估的，例如，用于单个工厂分割的面罩的“联合交叉点”（IoU）的平均平均精度（mAP）和用于检测的“多目标跟踪精度”（MOTA）。提出的模型达到了mAP 模型性能是使用直观的指标进行评估的，例如，用于单个工厂分割的面罩的“联合交叉点”（IoU）的平均平均精度（mAP）和用于检测的“多目标跟踪精度”（MOTA）。提出的模型达到了mAP 模型性能通过直观的指标进行评估，例如来自用于单个工厂分割的面罩的“联合交叉点”（IoU）的平均平均精度（mAP）和用于检测的多目标跟踪精度（MOTA）。提出的模型达到了mAP$0.418$ 用于马铃薯和 $0.660$用于单个植物分割任务的生菜。在检测中，我们获得的MOTA为$0.781$ 用于马铃薯和 $0.918$ 生菜。