Automation of pollen analysis using a computer microscope : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Systems Engineering at Massey University

The classification and counting of pollen is an important tool in the understanding of processes in agriculture, forestry, medicine and ecology. Current pollen analysis methods are manual, require expert operators, and are time consuming. Significant research has been carried out into the automation of pollen analysis, however that work has mostly been limited to the classification of pollen. This thesis considers the problem of automating the classification and counting of pollen from the image capture stage. Current pollen analysis methods use expensive and bulky conventional optical microscopes. Using a solid-state image sensor instead of the human eye removes many of the constraints on the design of an optical microscope. Initially the goal was to develop a single lens microscope for imaging pollen. In-depth investigation and experimentation has shown that this is not possible. Instead a computer microscope has been developed which uses only a standard microscope objective and an image sensor to image pollen. The prototype computer microscope produces images of comparable quality to an expensive compound microscope at a tenth of the cost. A segmentation system has been developed for transforming images of a pollen slide, which contain both pollen and detritus, into images of individual pollen suitable for classification. The segmentation system uses adaptive thresholds and edge detection to isolate the pollen in the images. The automated pollen analysis system illustrated in this thesis has been used to capture and analyse four pollen taxa with a 96% success rate in identification. Since the image capture and segmentation stages described here do not affect the classification stage it is anticipated that the system is capable of classifying 16 pollen taxa, as demonstrated in earlier research

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Natural Language Generation for Nature Conservation : Automating Feedback to help Volunteers identify Bumblebee Species

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De novo sequencing of the Hypericum perforatum L. flower transcriptome to identify potential genes that are related to plant reproduction sensu lato

Background: St. John's wort (Hypericum perforatum L.) is a medicinal plant that produces important metabolites with antidepressant and anticancer activities. Recently gained biological information has shown that this species is also an attractive model system for the study of a naturally occurring form of asexual reproduction called apomixis, which allows cloning plants through seeds. In aposporic gametogenesis, one or multiple somatic cells belonging to the ovule nucellus change their fate by dividing mitotically and developing functionally unreduced embryo sacs by mimicking sexual gametogenesis. Although the introduction of apomixis into agronomically important crops could have revolutionary implications for plant breeding, the genetic control of this mechanism of seed formation is still not well understood for most of the model species investigated so far. We used Roche 454 technology to sequence the entire H. perforatum flower transcriptome of whole flower buds and single flower verticils collected from obligately sexual and unrelated highly or facultatively apomictic genotypes, which enabled us to identify RNAs that are likely exclusive to flower organs (i.e., sepals, petals, stamens and carpels) or reproductive strategies (i.e., sexual vs. apomictic). Results: Here we sequenced and annotated the flower transcriptome of H. perforatum with particular reference to reproductive organs and processes. In particular, in our study we characterized approximately 37,000 transcripts found expressed in male and/or female reproductive organs, including tissues or cells of sexual and apomictic flower buds. Ontological annotation was applied to identify major biological processes and molecular functions involved in flower development and plant reproduction. Starting from this dataset, we were able to recover and annotate a large number of transcripts related to meiosis, gametophyte/gamete formation, and embryogenesis, as well as genes that are exclusively or preferentially expressed in sexual or apomictic libraries. Real-Time RT-qPCR assays on pistils and anthers collected at different developmental stages from accessions showing alternative modes of reproduction were used to identify potential genes that are related to plant reproduction sensu lato in H. perforatum. Conclusions: Our approach of sequencing flowers from two fully obligate sexual genotypes and two unrelated highly apomictic genotypes, in addition to different flower parts dissected from a facultatively apomictic accession, enabled us to analyze the complexity of the flower transcriptome according to its main reproductive organs as well as for alternative reproductive behaviors. Both annotation and expression data provided original results supporting the hypothesis that apomixis in H. perforatum relies upon spatial or temporal mis-expression of genes acting during female sexual reproduction. The present analyses aim to pave the way toward a better understanding of the molecular basis of flower development and plant reproduction, by identifying genes or RNAs that may differentiate or regulate the sexual and apomictic reproductive pathways in H. perforatum

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

Proline synthesis in developing microspores is required for pollen development and fertility

Background: In many plants, the amino acid proline is strongly accumulated in pollen and disruption of proline synthesis caused abortion of microspore development in Arabidopsis. So far, it was unclear whether local biosynthesis or transport of proline determines the success of fertile pollen development. Results: We analyzed the expression pattern of the proline biosynthetic genes PYRROLINE-5-CARBOXYLATE SYNTHETASE 1 & 2 (P5CS1 & 2) in Arabidopsis anthers and both isoforms were strongly expressed in developing microspores and pollen grains but only inconsistently in surrounding sporophytic tissues. We introduced in a p5cs1/p5cs1 p5cs2/P5CS2 mutant background an additional copy of P5CS2 under the control of the Cauliflower Mosaic Virus (CaMV) 35S promoter, the tapetum-specific LIPID TRANSFER PROTEIN 12 (Ltp12) promoter or the pollen-specific At5g17340 promoter to determine in which site proline biosynthesis can restore the fertility of proline-deficient microspores. The specificity of these promoters was confirmed by β-glucuronidase (GUS) analysis, and by direct proline measurement in pollen grains and stage-9/10 anthers. Expression of P5CS2 under control of the At5g17340 promoter fully rescued proline content and normal morphology and fertility of mutant pollen. In contrast, expression of P5CS2 driven by either the Ltp12 or CaMV35S promoter caused only partial restoration of pollen development with little effect on pollen fertility. Conclusions: Overall, our results indicate that proline transport is not able to fulfill the demand of the cells of the male germ line. Pollen development and fertility depend on local proline biosynthesis during late stages of microspore development and in mature pollen grains

Pollen DNA barcoding:Current applications and future prospects.

Identification of the species origin of pollen has many applications, including assessment of plant-pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.publishersversionPeer reviewe