Ramie Plant Counting Based on UAV Remote Sensing Technology and Deep Learning

Abstract

Plants number is an essential field phenotypic trait that affects the growth status and final quality of crop. In recent years, the integration of remote sensing technology and deep learning technology has provided a solution to the problem of crop plant counting in field. However, most of the previous studies have selected fixed crops (such as rice, wheat) for research, and few studies have reported the limitations in the application of this technology. In addition, as far as we know, there has been no report on the problem of ramie germplasm resources counting. In this study, in combination with DA (Data Augmentation) and three object detection algorithms, ramie germplasm resources were adopted to explore the accuracy of counting plant number under the condition of dense plant growth. The following functions were tested: (1) the influence of DA on the effect of plant counting; (2) the influence of ground sampling distance (GSD) on the effect of plant counting; (3) the influence of object detection algorithms on ramie detection object. The results showed that after the training sample was expanded by DA, the Precision of ramie plant counting model was increased by 6.630%. FCOS (Fully Convolutional One-Stage Object Detection) could perform better in small object and small sample data (Recall = 0.892, Precision = 0.819，RMSE = 0.089). It was necessary to ensure the consistency of GSD between training samples and verification samples for improving the accuracy of ramie plants counting. The ramie plant counting model has sufficient and stable ability to count ramie plants in the field, which can supplement the traditional manual counting method.

Keywords

• ramie
• plant counting
• object detection
• data augmentation
• uav
• rgb image