Automated Characterization of Plant Growth and Flowering Dynamics Using RGB Images

Guo, Wei

doi:10.1007/978-981-13-1065-2_23

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…This makes these characteristics of texture play a vital role in the diagnosis process. The same applies to many computer vision applications such as medical image analysis [11]- [13], satellite image analysis [14], factory image analysis [15], and computer graphics [16], [17]. In the sixties of the last century, many proposals were put forward on the issue of discrimination texture [18].…”

Section: Introductionmentioning

confidence: 95%

Local information pattern descriptor for corneal diseases diagnosis

Jameel

Aydin

Ghaeb

2021

IJECE

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<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may aﬀect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>

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Section: Introductionmentioning

confidence: 95%

Local information pattern descriptor for corneal diseases diagnosis

Jameel

Aydin

Ghaeb

2021

IJECE

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“…Plant phenotyping is a set of methodologies and protocols used to study plant performance, growth, architecture, and composition at different scales of organization, from organs to canopies, and it elucidates the plant trait dynamics in a non-destructive and non-invasive manner [5,14,[19][20][21]. Having great potential for application to commercial PFALs [22], imagebased studies in PFALs have been conducted, including quantifying the projected leaf area (value measured by 2D camera images captured from above the plant canopy or seedling trays) of the plant canopy [15,[23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Variations in the Growth of Cotyledons and Initial True Leaves as Affected by Photosynthetic Photon Flux Density at Individual Seedlings and Nutrients

Amagai

Kozai²,

Maruo³

et al. 2022

Agronomy

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Plant factories with artificial lighting (PFALs), with well-insulated and airtight structures, enable the production of large quantities of high-quality plants year-round while achieving high resource use efficiency. However, despite the controlled environment in PFALs, variations in plant individuals have been found, which affect productivity in PFAL operations. Plant phenotyping plays a crucial role in understanding how the surrounding microenvironment affects variations in plant phenotypes. In the current study, a modular phenotyping system for seedling production was developed, focusing on practicality and scalability in commercial PFALs. Experiments on seedlings, which strongly affect productivity, were conducted to obtain cotyledon unfolding time and the time series projected area of cotyledons and true leaves of individual seedlings of romaine lettuce (Lactuca sativa L. var. longifolia), using RGB images. This was also undertaken to analyze how the surrounding microenvironment of photosynthetic photon flux densities and nutrients affect growth variations for plant cohort research. In agreement with the actual measurements, variations in seedling growth were identified even under similar microenvironments. Furthermore, the results demonstrated larger variations in seedlings with higher relative growth. Aiming for simplified interactions of phenotypes with the microenvironment, management, and genotype, seedling selection and breeding with plant production in PFALs may enable plant uniformity and higher productivity.

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“…These measured traits may display macro-and micro-environmental variations [5]. Over the past years, the focus has been put on developing high-throughput accurate and rapid phenotyping systems together with image processing algorithms, which possesses potential to be adapted also for the study under controlled environmental conditions [1,6,7].…”

Section: Introductionmentioning

confidence: 99%

Phenotypic Analysis of Germination Time of Individual Seeds Affected by Microenvironment and Management Factors for Cohort Research in Plant Factory

Amagai

Maruo

Kozai³

2020

Agronomy

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Plant phenotyping plays a crucial role in understanding variations in the phenotype of individual plants affected by environment, management, and genotype. Measurement of seed germination is an important phenotyping stage as germination impacts on the whole plant growth process. However, germination measurement has been limited to germination percentage of a seed population. Understanding of the germination time, from sowing to outbreak of the radicle from seed coat, at a single seed level is essential. How individual germination time and further plant growth are affected by its microenvironment and management factors remains elusive. Plant phenotype measurement system was developed to assess individual germination time of romaine lettuce (Lactuca sativa L. var. longifolia), using time-series two-dimensional camera images, and to analyze how microenvironment (volumetric water percent in seed tray, individual seed surface temperature and air temperature) and management factors (coated/uncoated seeds) affect the germination time for plant cohort research, emphasizing practicality in commercial cultivation. Germination experiments were conducted to demonstrate the performance of the system and its applicability for a whole plant growth process in a plant factory for commercial production and/or breeding. The developed phenotyping platform revealed the effects of microenvironment and management factors on germination time of individual seeds.

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Automated Characterization of Plant Growth and Flowering Dynamics Using RGB Images

Cited by 4 publications

References 25 publications

Local information pattern descriptor for corneal diseases diagnosis

Local information pattern descriptor for corneal diseases diagnosis

Variations in the Growth of Cotyledons and Initial True Leaves as Affected by Photosynthetic Photon Flux Density at Individual Seedlings and Nutrients

Phenotypic Analysis of Germination Time of Individual Seeds Affected by Microenvironment and Management Factors for Cohort Research in Plant Factory

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