Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping

Tresch, Léa; Mu, Yun; Itoh, Atsushi; Kaga, Akito; Taguchi, Kazunori; Hirafuji, Masayuki; Ninomiya, S.; Guo, Wei

doi:10.1101/745752

Cited by 5 publications

(10 citation statements)

References 23 publications

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“…For our experiments we acquired orthomosaic images of sorghum and maize fields at various dates and altitudes. We evaluated our method (COPE) against EasyMPE [13] and the method described in [10] using our dataset. Note the typical orthomosaic images used in our experiment have more than 300 million pixels (e.g., 11500 × 47500 pixels).…”

Section: Resultsmentioning

confidence: 99%

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Field-Based Plot Extraction Using UAV RGB Images

Yang¹,

Baireddy²,

Cai³

et al. 2021

Preprint

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Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials. Field experiments, which may comprise thousands of plants, are planted according to experimental designs to evaluate varieties or management practices. For many types of phenotyping analysis, we examine smaller groups of plants known as "plots." In this paper, we propose a new plot extraction method that will segment a UAV image into plots. We will demonstrate that our method achieves higher plot extraction accuracy than existing approaches.

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Section: Resultsmentioning

confidence: 99%

“…Several plot extraction methods have been described in the literature. In [13], Tresch et al proposed a plot extraction method known as EasyMPE, which converts the field plant pixels into an energy map. Each plot is identified by thresholding the energy map.…”

Section: Introductionmentioning

confidence: 99%

Field-Based Plot Extraction Using UAV RGB Images

Yang¹,

Baireddy²,

Cai³

et al. 2021

Preprint

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“…To solve these issues, the enrichment of easy-to-use phenotyping tools to handle these processes is necessary. EasyIDP (Wang et al 2021a) for intermediate data processing for UAS images, EasyMPE (Tresch et al 2019) for microplot extraction, EasyPCC (Guo et al 2017) for crop segmentation, and EasyDCP (Feldman et al 2021) for 3D phenotyping are good examples. Then, we would need to integrate these tools as a pipeline on a common data exchange platform with standardized application programming interfaces (APIs) and access to genotypic data.…”

Section: Discussionmentioning

confidence: 99%

High-throughput field crop phenotyping: current status and challenges

Ninomiya

2022

Breed. Sci.

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In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, noninvasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.

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“…Furthermore, since height information might be helpful for late-season imagery, we could add the height information as another channel to our algorithm. Also, EasyMPE is a semiautomatic approach to obtain microplot information for drone imagery of whole fields [11]. The main part of EasyMPE is the microplot extraction part that is related to our work.…”

Section: Discussionmentioning

confidence: 99%

“…For many crops and field trial layouts, monitoring the complete growing season requires a robust method to capture images with little to no visible ground between microplots. Tresch et al developed a semiautomatic approach to obtain microplot information for drone imagery of whole fields using the microplot extraction method known as EasyMPE (microplot extraction) [11]. They were able to identify crop rows and columns in soybean and sugarbeet crops on six different field datasets.…”

Section: Introductionmentioning

confidence: 99%

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method

et al. 2021

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To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot’s alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

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Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping

Cited by 5 publications

References 23 publications

Field-Based Plot Extraction Using UAV RGB Images

Field-Based Plot Extraction Using UAV RGB Images

High-throughput field crop phenotyping: current status and challenges

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method

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