Real-time pollen monitoring using digital holography

We present the first validation of the SwisensPoleno, currently the only operational automatic pollen mon-itoring system based on digital holography. The device pro-vides in-flight images of all coarse aerosols, and here wedevelop a two-step classification algorithm that uses theseimages to identify a range of pollen taxa. Deterministiccriteria based on the shape of the particle are applied toinitially distinguish between intact pollen grains and othercoarse particulate matter. This first level of discriminationidentifies pollen with an accuracy of 96 %. Thereafter, in-dividual pollen taxa are recognized using supervised learn-ing techniques. The algorithm is trained using data obtainedby inserting known pollen types into the device, and out ofeight pollen taxa six can be identified with an accuracy ofabove 90 %. In addition to the ability to correctly identifyaerosols, an automatic pollen monitoring system needs to beable to correctly determine particle concentrations. To fur-ther verify the device, controlled chamber experiments us-ing polystyrene latex beads were performed. This providedreference aerosols with traceable particle size and numberconcentrations in order to ensure particle size and samplingvolume were correctly characterized

Automatic pollen recognition using convolutional neural networks: The case of the main pollens present in Spanish citrus and rosemary honey

[EN] The automation of honey pollen visual sorting overcomes the limitations of the conventional procedure helping the specialist in this time-consuming task. In this work, a novel and comprehensive Ground Truth of almost 19,000 images (from optical microscopy) of the 16 most abundant types of grains/pollen particles present in citrus and rosemary honey from Spain was constructed. This task was assisted by a HoneyApp (also developed herein) for the labelling and annotation process. Subsequently, the effectiveness of different pre-existing automatic pollen recognizers based on convolutional neural networks (CNN) (VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet201, MobileNetV2 and EfficientNetV2M) was tested together with a new network proposed in this paper (PolleNetV1). The extreme complexity of those pre-existing CNN and extensive use of millions of parameters makes this new proposal especially promising. Although with a slightly lower accuracy (average 96%) in determining the relative frequencies of different types of pollen grains/particles, it has considerable advantages such as simplicity and ability to be included in the future functionality to automate pollen recognition in honey. This is the first step to finally achieving an objective tool that allows the correct labelling of any types of pollen in honey, thus contributing to its transparency in the market.This work is part of Spanish project PID2019-106800RB-I00 (2019) with financial support from the Ministerio de Ciencia e Innovacion (MCIN), Agencia Estatal de Investigacion MCIN/AEI/10.13039/501100011033/. It has been also part of the project AGROALNEXT/2022/043, funded by the Next Generation European Union and the Plan de Recuperacion, Transformacion y Resiliencia of the Spanish Government, with the support of Generalitat Valenciana. The authors would like to thank the CRUE-Universitat Politecnica deValencia for providing the funds for open access publication.Valiente González, JM.; Juan-Borras, MDS.; López García, F.; Escriche Roberto, MI. (2023). Automatic pollen recognition using convolutional neural networks: The case of the main pollens present in Spanish citrus and rosemary honey. Journal of Food Composition and Analysis. 123:1-10. https://doi.org/10.1016/j.jfca.2023.10560511012

Automated Pollen Image Classification

This Master of Science thesis reviews previous research, proposes a method anddemonstrates proof-of-concept software for the automated matching of pollen grainimages to satisfy degree requirements at the University of Tennessee. An ideal imagesegmentation algorithm and shape representation data structure is selected, alongwith a multi-phase shape matching system. The system is shown to be invariantto synthetic image translation, rotation, and to a lesser extent global contrast andintensity changes. The proof-of-concept software is used to demonstrate how pollengrains can be matched to images of other pollen grains, stored in a database, thatshare similar features with up to a 75% accuracy rate

Computer Aspects of Solid Freeform Fabrication: Geometry, Process Control, and Design

Solid Freefonn Fabrication (SFF) is a class of manufacturing technologies aimed at the production of mechanical components without part-specific tooling or process planning. Originally used for creating modelsfor visualization, many industrial users of SFF technologies are realizing the greater potentialofSFF as legitimate manufacturing processes for producing patterns and, in some cases, functional.parts. Thus, SFF is becoming an important aspect of the product realization process in these industries. Solid Freefonn Fabrication arose from the dream of "push-button" prototyping, in which solid reproductions of three-dimensional geometric models are created automatically under computer control. Perhaps more than any other class of manufacturing technologies, computer software development has been an integral part of the emergence of SFF. As SFF technologies evolve toward the ability to create functional parts, computer issues gain more importance. This paper discusses three aspects of software design for SFF: processing of geometric data, global and local control of SFF processes, and computer-based analysis and design for SFF manufacturing. The discussion of geometric processing issues focuses on accuracy and completeness of input models, and the algorithms required to process such models. The interplay between the physics of SFF processing and the desired output geometry is discussed in terms of the development of model-based control algorithms for SFF. These two areas, geometric processing and control, are necessary for the practical implementation of any SFF technology. However, for SFF to realize its potential as an alternative for manufacturing functional parts, engineers must be provided with analysis and design tools for predicting mechanical properties, ensuring dimensional accuracy, choosing appropriate materials, selecting process parameter values, etc. For each of these three different but related areas of software design, the state-of-theart is assessed, contemporary research is summarized, and future needs are outlined.Mechanical Engineerin

Verification of a Lagrangian pollen dispersion model and sensitivity to particle size and environmental conditions

There is controversy in using genetically modified crops. And there are two sides to this controversy. One side wants to grow 100% genetically modified corn. The other side wants to grow organic crops. The development of a Lagrangian pollen dispersion model will help determine the amount of outcross in open pollinated crops. By inputting the wind direction, wind speed, atmospheric stability and the location of cornfields the model can be used to predict the percentage of outcross in the field. Once the pollen dispersion model is completed, corn growers can use it to find the percentage of outcross in their field. This model can also be used for field setup; it can help determine the distance from adjacent cornfields the grower should plant. The model can also be used to predict the amount of outcross in a field. This study found that the Lagrangian Pollen Dispersion Model was able to predict the direction and concentration of maize pollen. The concentration values did not match with the observations exactly, but in general the model predicted pollen dispersion where the observations showed pollen concentration. Pollen dispersion is sensitive to pollen grain size. Larger pollen grains land closer to the source field because of the larger terminal fall speed associated with the pollen grains and the opposite for smaller pollen grains. A pollen size distribution in the model matches with observations the best. This shows that a pollen size distribution should be used when accounting for pollen terminal fall speed. Sensitivity tests on wind speed and atmospheric stability showed that stronger wind speeds caused the pollen grains to land farther from the source field than lighter wind speeds and that stronger instability also caused pollen grains to land farther from the source field than under neutral or weak instability conditions. With wind speeds greater to 2 m s−1, increasing atmospheric instability did not change the distribution of the pollen grains. With wind speeds less than 2 m s−1, increasing atmospheric instability caused the pollen grains to travel farther from the source field

The predictive power of pollination syndromes: Passerine pollination in heterantherous Meriania macrophylla (Benth.) Triana (Melastomataceae)

The cloud forest species Meriania macrophylla (Benth.) Triana has pseudocampanu- late flowers with bulbous stamen appendages, typical for the passerine pollination syndrome found in the Melastomataceae tribe Merianieae. The species is further characterized by strong stamen dimorphism (heteranthery), a condition otherwise associated with pollen-rewarding bee-pollinated species (both in Melastomataceae and beyond). In passerine-pollinated Merianieae, however, flowers usually only show weak stamen dimorphism. Here, we conducted field and laboratory investigations to determine the pollinators of M. macrophylla and assess the potential role of strong heteranthery in this species. Our field observations in Costa Rica confirmed syn- drome predictions and indeed proved pollination by passerine birds in M. macrophylla. The large bulbous set of stamens functions as a food-body reward to the pollinating birds, and as trigger for pollen release (bellows mechanism) as typical for the pas- serine syndrome in Merianieae. In contrast to other passerine-pollinated Merianieae, the second set of stamens has seemingly lost its rewarding and pollination function, however. Our results demonstrate the utility of the pollination syndrome concept even in light of potentially misleading traits such as strong heteranthery.Austrian Science Fund/[P-30669]/FWF/AustriaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Estructuras Microscópicas (CIEMIC)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de BiologíaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biodiversidad y Ecología Tropical (CIBET

Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis

Airborne pollen is produced by plants for their sexual reproduction and can have negative impacts on public health. The current monitoring systems are based on manual sampling processes which are tedious and time-consuming. Due to that, pollen concentrations are often reported with a delay of up to one week. In this study, we present an open-source user-friendly web application powered by deep learning for automatic pollen count and classification. The application aims to simplify the process for non-IT users to count and classify different types of pollen, reducing the effort required compared to manual methods. To overcome the challenges of acquiring large labelled datasets, we propose a semi-automatic labelling approach, which combines human expertise and machine learning techniques. The results demonstrate that our approach significantly reduces the effort required for users to count and classify pollen taxa accurately. The model achieved high precision and recall rates (> 96% [email protected]), enabling reliable pollen identification and prediction.Funding for open access charge: Universidad de Málaga/CBUA. This work was financed by the Ministry of Science and Innovation of Spain and FEDER funding inside the Operational Plurir- regional Program of Spain 2014–2020 and the Operational Program of Smart Growing (Environmental and Biodiversity Climate Change Lab, EnBiC2-Lab; LIFEWATCH-2019-11-UMA-01-BD) and by the Span- ish project TED2021-130167B-C33 (‘GEDIER: Application of Digital Twins to more sustainable irrigated farms’). A. Picornell was supported by a postdoctoral grant financed by the Ministry of Economic Transfor- mation, Industry, Knowledge and Universities of the Junta de Andalucía (POSTDOC_21_00056)

Best Practice Documents for coexistence of genetically modified crops with conventional and organic farming. 2. Monitoring efficiency of coexistence measures in maize crop production

The present technical report deals with monitoring the efficiency of measures/strategies for coexistence between genetically modified (GM) and non-GM maize crop production. The report is a follow up of the best practices for coexistence in maize crop production proposed by the Technical Working Group (TWG) for Maize of the European Coexistence Bureau (ECoB). The ECoB TWG maize held three meetings in October 2010, June 2012 and November 2012 and examined state-of-art-knowledge from scientific literature, research projects and empirical evidence provided by numerous finished and ongoing studies looking at the appropriate level of monitoring, monitoring strategy, sampling and testing issues, detection methods, analysis of results and possible follow up. The review of this information (coming from a total of 55 references) is presented in a structured manner in Section 3 and 4 of the document. The overview of the activities carried out by EU Member States for monitoring effectiveness/efficiency of coexistence measures in maize crop production (Section 3), shows a still limited experience in practical terms, due to the limited experience in commercial cultivation of GM maize in most EU Member States. However, the present report provides technical guidance to those responsible for monitoring the efficiency of coexistence strategies.JRC.J.4-Agriculture and Life Sciences in the Econom

Cultural Implications of Late Quaternary Environmental Change in Northeastern Texas

Northeastern Texas is one of the most intensely studied archaeological regions of the state, principally for the two reasons that (1) archaeologically-rich Caddoan manifestations have long attracted interest and (2) many large land-modifying projects, such as reservoirs and strip mines, have occasioned environmental studies which include investigations of cultural resources. This greater amount of activity relative to other regions in the state has generally prevailed for more than a century (Guy 1990) and prospects are good that archaeologists will continue to intensely research the area in the foreseeable future. Unfortunately, however, they will be hard-pressed to keep pace with the destruction of archaeological sites. The region is growing in population and developing economically which inevitably results in land modifications destructive of archaeological evidence. Also, many sites are being willfully destroyed by commercial dealers in antiquities and by relic collectors. This contribution to historic preservation planning is concerned with gaining a better understanding of the past environmental factors that stimulated responses by past inhabitants of Northeastern Texas, and the natural environmental context of the archaeological record in Northeastern Texas. The area has some overall environmental similarities--such as being in a single physiographic section, and falling within a single climatic region--that might be misconstrued to imply a uniformity of conditions critical in pre-industrial human adaptations. In fact, the conditions important in human ecology vary significantly across space and have varied equally significantly over time