Image Processing for Pollen Classification

Pozo-Baños, Marcos del; Ticay-Rivas, Jaime R.; Cabrera-Falcón, Jousé; Arroyo, Jorge; González, Carmelo Javier León; Sánchez-Chavez, Luis; Pérez, Santiago T.; Alonso, Jesús B.; Ramírez-Bogantes, Melvín

doi:10.5772/48456

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Two are texture related, providing information about how pixel intensities are distributed on the image, and the others are associated with the color content from two different color spaces, HSV and RGB, respectively. Most features were explored in [29]. The complete list is described in the S1 Table . Differences between the viable and non-viable pollen grains are easily perceived visually (Fig 3A).…”

Section: Plos Onementioning

confidence: 99%

Physiological and genetic characterization of heat stress effects in a common bean RIL population

et al. 2021

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Heat stress is a major abiotic stress factor reducing crop productivity and climate change models predict increasing temperatures in many production regions. Common bean (Phaseolus vulgaris L.) is an important crop for food security in the tropics and heat stress is expected to cause increasing yield losses. To study physiological responses and to characterize the genetics of heat stress tolerance, we evaluated the recombinant inbred line (RIL) population IJR (Indeterminate Jamaica Red) x AFR298 of the Andean gene pool. Heat stress (HS) conditions in the field affected many traits across the reproductive phase. High nighttime temperatures appeared to have larger effects than maximum daytime temperatures. Yield was reduced compared to non-stress conditions by 37% and 26% in 2016 and 2017 seasons, respectively. The image analysis tool HYRBEAN was developed to evaluate pollen viability (PolVia). A significant reduction of PolVia was observed in HS and higher viability was correlated with yield only under stress conditions. In susceptible lines the reproductive phase was extended and defects in the initiation of seed, seed fill and seed formation were identified reducing grain quality. Higher yields under HS were correlated with early flowering, high pollen viability and effective seed filling. Quantitative trait loci (QTL) analysis revealed a QTL for both pod harvest index and PolVia on chromosome Pv05, for which the more heat tolerant parent IJR contributed the positive allele. Also, on chromosome Pv08 a QTL from IJR improved PolVia and the yield component pods per plant. HS affected several traits during the whole reproductive development, from floral induction to grain quality traits, indicating a general heat perception affecting many reproductive processes. Identification of tolerant germplasm, indicator traits for heat tolerance and molecular tools will help to breed heat tolerant varieties to face future climate change effects.

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Section: Plos Onementioning

confidence: 99%

Physiological and genetic characterization of heat stress effects in a common bean RIL population

et al. 2021

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Pattern recognition methodologies for pollen grain image classification: a survey

Viertel

König

2022

Machine Vision and Applications

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In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study of pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since the mid-nineties of the last century, the idea for an automated solution to the problem of pollen identification and classification was formulated and since then, several attempts and proposals have been made and presented, based on different technologies, in particular in the field of Computer Vision. However, as of 2021 microscopic analyses are performed mainly manually by highly trained specialists, although the capabilities of artificial intelligence, especially Deep Neural Networks, are steadily increasing. In this work, we analyzed various state-of-the-art research work concerning pollen detection and classification and compared their methods and results. The problems, such as data accessibility, different methods of Machine Learning, and the intended applicability of the proposed solutions are explored. We also identified crucial issues that require further work and research. Our work will provide a thorough view on the current state of the art, its issues, and possibilities for the future.

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A new method to quantify atmospheric Poaceae pollen DNA based on the trnT-F cpDNA region

Alan

Sarışahin

Şahin

et al. 2018

Turkish Journal of Biochemistry

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Background Pollen, mold spores, bacteria and viruses are the main biological substances in the atmosphere causing allergic symptoms and disease. Distinguishing pollen and spores is quite time consuming and requires a trained expert. There is a different approach to identification of these substances such as microscopic analysis. However, DNA based identification of these is becoming popular recently. Objective We evaluated the correlation between the quantity of DNA, which was amplified using trnT-F cpDNA specific primers in samples obtained from a high volume air sampler (HVAS), and concentration of Poaceae pollen collected with a Burkard trap. Materials and methods Here, we present a method for identifying and quantifying airborne Poaceae pollen using a single step polymerase chain reaction (PCR) technique. Forty daily air samples were collected by HVAS. The method was optimised using two different methods (M1 and M2) and the trnT-F cpDNA region was amplified using a Poaceae specific primer pair. The correlation between the quantity of DNA and pollen concentration was tested using R statistical programming language. Results Although a significant correlation was obtained between the M1 and M2 methods (R2=0.655, p<0.01), the M2 method was more correlated with pollen concentration. The correlation between pollen and DNA content changed due to episodes that were observed during the pollen season. DNA concentrations from the PCR data were significantly correlated with pollen concentrations determined by light microscopy (R2=0.767, p<0.01) in episode II using the M2 method and during the entire season (R2=0.469, p<0.01) using M2. Conclusions The M2 method correctly identified Poaceae pollen in mixed air samples from Zonguldak Province. The non-coding trnT-F cpDNA region was used for the first time in aerobiological samples to identify Poaceae pollen. Use of this method that does not require DNA extraction may be a crucial step for real-time pollen monitoring devices to be developed in the future. The correlation strength between pollen and amplified DNA content could be improved using a sampler that has a lower absorption rate, and a more sensitive technique, such as qPCR.

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Image Processing for Pollen Classification

Cited by 6 publications

References 15 publications

Physiological and genetic characterization of heat stress effects in a common bean RIL population

Physiological and genetic characterization of heat stress effects in a common bean RIL population

Pattern recognition methodologies for pollen grain image classification: a survey

A new method to quantify atmospheric Poaceae pollen DNA based on the trnT-F cpDNA region

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