ArticleApplications in plant sciences2020
Robust mosaicking of maize fields from aerial imagery.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics.Sensors (Basel, Switzerland) · 2021Article
- Corrigendum.Applications in plant sciences · 2020Article
- Robust mosaicking of maize fields from aerial imagery.Applications in plant sciences · 2020Article
Corrections and comments
- Erratum issuedCorrigendum.2020
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseAerial imagery from small unmanned aerial vehicle systems is a promising approach for high-throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field-scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery.
methodsWe have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two-dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame-to-frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two-dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (
resultsThe VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest-quality mosaics. DISCUSSION: Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management.
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