EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

Haozhou Wang,Yoichiro Kato,Yun Shi,Wei Guo,Yulin Duan,Seishi Ninomiya

doi:10.3390/rs13132622

Abstract

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

Highlights

Introduction iationsCompared with traditional manual field mensuration, which is time consuming, labor intensive, and subjective, the recently emerged 3D reconstruction technologies provide a non-destructive and high-throughput solution for plant phenotyping [1,2,3]
Human intervention is required for those raw images for which ground control points (GCPs) cannot be precisely and automatically detected, some pipelines have been proposed to make this step automated [11,12,13]
For dataset 5, three distinguishable corners of the field were used as GCPs

Summary

Introduction

Compared with traditional manual field mensuration, which is time consuming, labor intensive, and subjective, the recently emerged 3D reconstruction technologies provide a non-destructive and high-throughput solution for plant phenotyping [1,2,3]. As one branch of 3D reconstruction, photogrammetry (structure from motion and multi-view stereo (SfMMVS)) technology, which requires only a standard digital camera, has been widely used on both ground [4,5,6] and unmanned aerial vehicle (UAV) platforms [7,8] in the open field. The general workflow of UAV-based plant phenotyping by photogrammetry is demonstrated in Figure 1 with four main parts: Licensee MDPI, Basel, Switzerland. Human intervention is required for those raw images for which GCPs cannot be precisely and automatically detected, some pipelines have been proposed to make this step automated [11,12,13]

Methods

Results

Discussion

Conclusion