Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.

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

Remote Sensing ◽

10.3390/rs13132622 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2622

Author(s):

Haozhou Wang ◽

Yulin Duan ◽

Yun Shi ◽

Yoichiro Kato ◽

Seish Ninomiya ◽

...

Keyword(s):

Data Processing ◽

Plant Phenotyping ◽

Phenotypic Traits ◽

Data Annotation ◽

Intermediate Data ◽

Reconstruction Quality ◽

Open Source Software Package ◽

Machine Learning Applications ◽

Data Processor ◽

Uav Images

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.

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