High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

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High-throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities

Annals of Botany ◽

10.1093/aob/mcs245 ◽

2012 ◽

Vol 112 (2) ◽

pp. 381-389 ◽

Cited By ~ 50

Author(s):

Lei Shi ◽

Taoxiong Shi ◽

Martin R. Broadley ◽

Philip J. White ◽

Yan Long ◽

...

Keyword(s):

Brassica Napus ◽

High Throughput ◽

Genetic Loci ◽

Root Phenotyping ◽

Architectural Traits

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Haplotype analysis of data from UAV imagery of rice MAGIC population for the trait dissection of biomass and plant architecture

Journal of Experimental Botany ◽

10.1093/jxb/eraa605 ◽

2020 ◽

Author(s):

Daisuke Ogawa ◽

Toshihiro Sakamoto ◽

Hiroshi Tsunematsu ◽

Noriko Kanno ◽

Yasunori Nonoue ◽

...

Keyword(s):

High Resolution ◽

High Throughput ◽

Early Stage ◽

Association Studies ◽

Image Data ◽

Shoot Biomass ◽

Genome Wide Association Studies ◽

Vegetation Fraction ◽

High Throughput Phenotyping ◽

Uav Images

Abstract Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four QTL for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTL. Further genetic characterization revealed the relationships between these QTL and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stage of rice in the field provides insight into plant growth, architecture, final biomass and yield.

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

Plant Phenomics ◽

10.34133/2019/2591849 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Léa Tresch ◽

Yue Mu ◽

Atsushi Itoh ◽

Akito Kaga ◽

Kazunori Taguchi ◽

...

Keyword(s):

Image Processing ◽

Image Quality ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Field Conditions ◽

Processing Step ◽

Field Phenotyping ◽

Binary Segmentation ◽

Aerial Vehicle ◽

Uav Images

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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An Efficient Method for Estimating Wheat Heading Dates Using UAV Images

Remote Sensing ◽

10.3390/rs13163067 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3067

Author(s):

Licheng Zhao ◽

Wei Guo ◽

Jian Wang ◽

Haozhou Wang ◽

Yulin Duan ◽

...

Keyword(s):

High Throughput ◽

Growth Curve ◽

Large Scale ◽

Agricultural Research ◽

Heading Date ◽

Absolute Error ◽

Growth Cycle ◽

Continuous Growth ◽

Large Scale Field ◽

Uav Images

Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas.

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

10.1101/745752 ◽

2019 ◽

Author(s):

Léa Tresch ◽

Yue Mu ◽

Atsushi Itoh ◽

Akito Kaga ◽

Kazunori Taguchi ◽

...

Keyword(s):

Image Processing ◽

Image Quality ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Automatic Program ◽

Processing Step ◽

Field Phenotyping ◽

Binary Segmentation ◽

Aerial Vehicle ◽

Uav Images

AbstractMicroplot extraction (MPE) 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 semi-automatic 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 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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Assessing T-LiDAR technology for high throughput phenotyping apple tree topological and architectural traits

Acta Horticulturae ◽

10.17660/actahortic.2020.1281.82 ◽

2020 ◽

pp. 625-632

Author(s):

B. Pallas ◽

S. Martinez ◽

O. Simler ◽

E. Carrié ◽

E. Costes ◽

...

Keyword(s):

High Throughput ◽

Apple Tree ◽

High Throughput Phenotyping ◽

Architectural Traits

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Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Remote Sensing ◽

10.3390/rs11192209 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2209 ◽

Cited By ~ 8

Author(s):

Ethan L. Stewart ◽

Tyr Wiesner-Hanks ◽

Nicholas Kaczmar ◽

Chad DeChant ◽

Harvey Wu ◽

...

Keyword(s):

Deep Learning ◽

High Throughput ◽

Plant Disease ◽

Ground Truth ◽

Leaf Blight ◽

Northern Leaf Blight ◽

Average Precision ◽

Global Food Security ◽

Serious Disease ◽

Uav Images

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

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