Research Article: Extraction of Small Spatial Plots from Geo-Registered UAS Imagery of Crop Fields

Hearst, Anthony; Cherkauer, Keith A.

doi:10.1017/s1466046615000162

Cited by 9 publications

(10 citation statements)

References 10 publications

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“…It automatically requires crops to be rectangular-shaped, and adding a grid can be impacted by human perception, adding possible errors. It does not provide any additional services in [ 9 ], the process can be fully automatic if the process and GPS localization are extremely precise (RTK recommended), and the field can access a high level of technology and informatics competencies …”

Section: Discussionmentioning

confidence: 99%

“…Several semiautomatic techniques have been developed to extract microplots from UAV images. A method proposed by Hearst [ 9 ] takes advantage of device interconnectivity by using the information on the geolocalization of crop rows from the seeder. The resulting orthomosaic thus contains spatial landmarks that allow it to be divided into microplots.…”

Section: Introductionmentioning

confidence: 99%

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Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

Tresch

Itoh

et al. 2019

Plant Phenomics

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Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

Tresch

Itoh

et al. 2019

Plant Phenomics

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“…If accurate geo-registration accompanies the image acquisition process of UAP, precise micro-plot extractions should be greatly improved (Hearst, 2014 ). To achieve this goal, he sensor configuration pattern must also undergo improvement.…”

Section: Ht3p For Field Phenotyping In Notoriously Heterogeneous Condmentioning

confidence: 99%

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Quan

Song

et al. 2021

Front. Bioeng. Biotechnol.

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Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

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“…Grid or pattern‐based plot extraction is widely used in the literature for plot delineation (Anderson et al., 2019; Chapman et al., 2014; Drover et al. 2018; Duan, Chapman, Guo, & Zheng, 2017; Haghighattalab et al., 2016; Hearst & Cherkauer, 2015; Holman et al., 2016). The strength of this method is the ease of outlining large fields with regular dimensions, though manual plot adjustment is required as fields are not as geometrically consistent as plot maps.…”

Section: Introductionmentioning

confidence: 99%

Plot extraction from aerial imagery: A precision agriculture approach

Bruce

Rajcan

Sulik

2020

The Plant Phenome Journal

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The plant phenotyping community is adopting technological innovations in order to record phenotypic attributes more quickly and objectively. Low altitude aerial imaging is an appealing option for increasing throughput but there are still challenges in the image processing pipeline. One such challenge involves the assignment of a spatial reference to each plot entry in an experimental layout. Image-based approaches are increasingly popular since plot boundaries are often, but not always, clearly visible in low altitude imagery. In addition, workflows that make geometric assumptions about plot layout also show promise. We outline an alternative approach to generate plot boundaries to overlay with aerial imagery. The proposed method involves highaccuracy georeferencing (i.e., within a few cm) of imagery and planter activity, after which georeferencing of all plot entries is complete and only requires a few simple steps to convert logged spatial positions to polygons using open source geographic information systems (GIS) software. Compared with other approaches, the proposed method provides imagery that is precisely aligned over time and always aligns with plot boundaries, which are fixed and do not vary from image to image.

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Research Article: Extraction of Small Spatial Plots from Geo-Registered UAS Imagery of Crop Fields

Cited by 9 publications

References 10 publications

Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Plot extraction from aerial imagery: A precision agriculture approach

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