Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

Pérez-Sánz, Fernando; Navarro, Pedro J.; Egea‐Cortines, Marcos

doi:10.1093/gigascience/gix092

Cited by 141 publications

(112 citation statements)

References 189 publications

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“…However, in comparison to genomics analyses, pipelines for image processing are far less standardized and represent a major informatics challenge. As these phenomics approaches have become more widely adopted, a number of tools for image processing from small‐scale glasshouse experiments to large hyperspectral field experiments have been developed (Perez‐Sanz et al ., ). Machine learning is a promising approach to accelerate data processing and improve trait analysis (Singh et al ., ), although obtaining high‐quality annotated images will also be essential (Tsaftaris et al ., ).…”

Section: Developments In Phenotypingmentioning

confidence: 97%

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

2018

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Summary Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in‐field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome‐level assembly of the wheat genome now facilitates a step‐change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene‐editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

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Section: Developments In Phenotypingmentioning

confidence: 97%

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

2018

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“…It focuses on the sensors that directly measure morphological features, and not on the sensors that rely on a relation between the architectural traits and reflectance. Several recent papers already compare the performances of the most common 3D sensors for high throughput plant phenotyping (Li et al, 2014;Vázquezarellano et al, 2016;Perez-Sanz et al, 2017;Qiu et al, 2018;Wang et al, 2018). Based on these reviews, stereo vision is perceived as sensitive to sunlight and poorly adapted for outdoor imaging.…”

Section: Comparison With Other Proximal 3d Sensorsmentioning

confidence: 99%

“…To overcome the double challenge to increase crop yield while limiting inputs, the development of high-throughput nondestructive phenotyping methods has emerged as a hot research topic. Many advancements have been made for indoor high-throughput set-ups (Perez-Sanz et al, 2017), whereas natural conditions such as wind or the variability of sunlight pose challenges for outdoor image acquisition and related treatment. In the field, the extraction of plant traits from a canopy structure also remains a complex task due to organ overlapping, especially for dense crops such as cereals.…”

Section: Introductionmentioning

confidence: 99%

“…As the whole scene is illuminated simultaneously, time of flight cameras solve the scanning time problem. They are suitable for indoor measurements but the need of active light diminishes the performances of image acquisition under strong sunlight (Kazmi et al, 2012;Perez-Sanz et al, 2017). Binocular stereo vision relies on two cameras to compute depth by triangulation.…”

Section: Introductionmentioning

confidence: 99%

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Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction

et al. 2020

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Stereo vision is a 3D imaging method that allows quick measurement of plant architecture. Historically, the method has mainly been developed in controlled conditions. This study identified several challenges to adapt the method to natural field conditions and propose solutions. The plant traits studied were leaf area, mean leaf angle, leaf angle distribution, and canopy height. The experiment took place in a winter wheat, Triticum aestivum L., field dedicated to fertilization trials at Gembloux (Belgium). Images were acquired thanks to two nadir cameras. A machine learning algorithm using RGB and HSV color spaces is proposed to perform soil-plant segmentation robust to light conditions. The matching between images of the two cameras and the leaf area computation was improved if the number of pixels in the image of a scene was binned from 2560 × 2048 to 1280 × 1024 pixels, for a distance of 1 m between the cameras and the canopy. Height descriptors such as median or 95th percentile of plant heights were useful to precisely compare the development of different canopies. Mean spike top height was measured with an accuracy of 97.1 %. The measurement of leaf area was affected by overlaps between leaves so that a calibration curve was necessary. The leaf area estimation presented a root mean square error (RMSE) of 0.37. The impact of wind on the variability of leaf area measurement was inferior to 3% except at the stem elongation stage. Mean leaf angles ranging from 53°to 62°were computed for the whole growing season. For each acquisition date during the vegetative stages, the variability of mean angle measurement was inferior to 1.5% which underpins that the method is precise.

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“…Some of these include various UAS platforms [17,18], field-based robotic phenotyping system [19], unmanned aerial system [20], ultrasonic sensors [21], the light detection and ranging (LiDAR) [22], the time of flight cameras [23], tomography imaging [24], Kinect v2 camera [21], RGB and NIR imaging [25], and Phenobot 1.0 [26]. The next-generation phenomics tools generate enormous amount of data that are being translated via machine-learning statistical approaches into trait descriptions, relevant to sorghum breeders [27].…”

Section: Analyzing Sorghum Biomass Potential 21 Phenotyping Biomass mentioning

confidence: 99%

Sorghum an Important Annual Feedstock for Bioenergy

Sadia¹,

Awan²,

Saleem³

et al. 2019

Biomass for Bioenergy - Recent Trends and Future Challenges

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Plant-based renewable biofuels guarantee sustainable solutions to food and energy demands. High-biomass C4 grasses including sugarcane, corn, and sorghum are potential candidates for bioenergy. Among these, sorghum enjoys the status of a highly diverse food, feed, and biofuel source worldwide. The natural attributes like abiotic stress tolerance, diverse genetic base, viable seed industry, and sound breeding system make sorghum a perfect candidate for establishing an efficient and low-cost biofuel industry. Scientists are exploring ways to exploit forage, sweet, and biomass sorghums as climate-smart energy crops. In this context, conventional breeding has played a significant role in developing high-yielding sorghum varieties. For biomass sorghum, stem compositional analysis helps screen low lignin and high polysaccharide types as feedstocks for biofuels. Recent tools of phenomics, genomics, proteomics, and genome editing are key players of designing eco-friendly bioenergy sorghum. Here, we report stem compositional analysis and proteomicsbased evaluation of USDA sorghum germplasm as a baseline to develop sorghum as a biofuel feedstock.

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Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

Cited by 141 publications

References 189 publications

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction

Sorghum an Important Annual Feedstock for Bioenergy

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