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

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 13; p. 2622
• Main Authors: Wang, Haozhou, Duan, Yulin, Shi, Yun, Kato, Yoichiro, Ninomiya, Seishi, Guo, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Accuracy; Agisoft Metashape; Annotations; Automation; Batch processing; Computer programs; Data processing; Datasets; Digital cameras; Digital imaging; Geographic information systems; Geography; Image reconstruction; Learning algorithms; Machine learning; Microprocessors; Modules; Open source software; orthomosaic; Phenotyping; Photogrammetry; Pix4D; Quality assessment; Quality control; Remote sensing; reverse calculation; Software; Sorghum; Unmanned aerial vehicles; Workflow; Workload; Workloads; United States > US; Switzerland; China; Russia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13132622

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.