Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

Bekkering, Cody S.; Huang, Jin; Tian, Li

doi:10.3390/agronomy10091287

Cited by 14 publications

(6 citation statements)

References 127 publications

(163 reference statements)

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“…This is especially important for understanding microbe‐mediated adaptive plasticity, which cannot be definitively established without also measuring traits. There are many competing software packages to measure root traits but each of these programs struggles with large, complicated root systems that are often imperfectly excavated from natural systems (Bekkering et al, 2020).…”

Section: Implications and Future Research Directionsmentioning

confidence: 99%

Microbe‐mediated adaptation in plants

2021

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Plant-associated microbes affect a wide-variety of plant functional traits, and thus they likely affect patterns of plant local adaptation. However, the role of microbes in plant local adaptation is rarely tested. In a survey of the plant local adaptation literature, I found that the vast majority of studies that report local adaptation (94%) performed transplants into microbe-containing soils and measured traits that are microbemediated. In these studies, microbe-mediated effects are confounded with plant genotype effects. For my dissertation research, I sought to describe the phenomenon of microbe-mediated adaptation and provide a definitive test using a modified reciprocal transplant experiments that moved seeds and microbes into two habitats. Here I present my dissertation work in three chapters:In my first chapter, I propose two ways in which microbes facilitate patterns of adaptation for plants, microbe-mediated local adaptation and microbe-mediated adaptive plasticity. Microbe-mediated local adaptation is when plant genotypes have higher fitness at their home site because of a genotype-specific affiliation with locally important microbe(s). Microbe-mediated adaptive plasticity is when a plant genotype has higher fitness in multiple habitats because it demonstrates plasticity in associating with locally important microbes.In my second chapter, I describe soil conditions, plant phenotype, and microbial communities in two habitats. I used high-throughput tagged amplicon sequencing of SSU-rRNA genes (for bacteria) and ITS1 (for fungi) to characterize the root microbiome of Saint John's wort, Hypericum perforatum. At each root sampling location, I quantified soil characteristics to evaluate habitat variables that are correlated with root microbial communities. I also collected information on plant growth and fecundity for each focal plant to identify microbes that are correlated with plant phenotype. We found that bacterial and fungal communities were significantly different between habitats. Community differences were correlated with soil nitrogen and pH, and two taxa in the phylum Actinobacteria were significantly correlated with plant height.In my third chapter, I test for microbe-mediated adaptation using the two habitats, limestone barrens (alvars) and old-fields, described in chapter two. I conducted reciprocal transplant experiments in two years, exploring microbe effects on plant germination, survival, and growth. I found that alvar seeds had higher probability of germination when transplanted into home soils with microbes, but only when transplanted into their home habitat. Similarly, alvar seedlings had a higher probability of survival when transplanted into their home live soils, but here transplant site was unimportant. I also found local microbes inhibited local adaptation for old-field seedlings and facilitated local adaptation for alvar seedlings. My results indicate that microbial mutualists were important for plants growing in marginal alvar habitats, and that microbes changed patterns of local...

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Section: Implications and Future Research Directionsmentioning

confidence: 99%

Microbe‐mediated adaptation in plants

2021

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“…1 illustrates a collection of imaging techniques and scales in plant phenotyping. Generally, the aforementioned imaging techniques can also fall into organ-level [54], canopy-level [55], and remote sensing categories [56].…”

Section: Image-based Phenotypingmentioning

confidence: 99%

Deep Learning for Image-Based Plant Growth Monitoring: A Review

Tong

Lee²,

Yen

2022

Int. j. eng. technol. innov.

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Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.

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“…The plant phenotyping parameter is an essential part for highthroughput phenotyping studies. Recent studies show that the phenotyping parameters of economic crops (including barley [24], wheat [25], sorghum [26], maize [27], tomato [28], bean [29] and cotton [30]) are taken into account. These plant phenotyping parameters can be grouped into two categories: morphometric and physiological [22].…”

Section: B Sensing Parameters For High-throughput Plant Phenotypingmentioning

confidence: 99%

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Rayhana

Feng

et al. 2022

IEEE Open J. Instrum. Meas.

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The current epidemic, population growth, and decreasing arable lands lead to a severe food crisis, which calls for productive and efficient agricultural methods to ensure a sustainable food supply for mankind. Crop monitoring is considered to be a potential solution for the improvement of food production. Current crop monitoring combines agriculture methodologies with other advanced technologies, including sensing technology, geographical information systems (GIS), Internet of Things (IoT), information and communication technology (ICT), robotics, and drone techniques to increase production with low labor cost. The high-throughput plant phenotyping is crucial for crop monitoring on the data acquisition of large-scale crop characteristics. The high-throughput plant phenotyping studies aim to achieve fast and precise large-scale crop monitoring techniques with minimum environmental impact by applying special plant phenotyping platforms. The phenotyping platforms are integrated with various sensors and data communication systems, which can help to achieve automatic data acquisition and transmission. This paper reviews the current high-throughput plant phenotyping development in crop monitoring, including sensors, communication protocols, data management, and plant phenotyping platforms. State-of-art challenges are reviewed and discussed. Also, the paper provides discussions on the current situation, upcoming challenges, and possible future trends for researchers in this field.

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Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

Cited by 14 publications

References 127 publications

Microbe‐mediated adaptation in plants

Microbe‐mediated adaptation in plants

Deep Learning for Image-Based Plant Growth Monitoring: A Review

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

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