New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Lin, Yi-Chun; Zhou, Tian; Wang, Taojun; Crawford, Melba; Habib, Ayman

doi:10.3390/rs13050860

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Journal Article · Thu Feb 25 00:00:00 EST 2021 · Remote Sensing

DOI:https://doi.org/10.3390/rs13050860· OSTI ID:1768076

; ; Wang, Taojun; Crawford, Melba;

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

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Research Organization:: Purdue University, West Lafayette, IN (United States)

Sponsoring Organization:: USDOE; USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000593

OSTI ID:: 1768076

Alternate ID(s):: OSTI ID: 1848209

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 5 Vol. 13; ISSN 2072-4292; ISSN RSBSBZ

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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