scholarly journals Discernment of bee pollen loads using computer vision and one-class classification techniques

2012 ◽  
Vol 112 (1-2) ◽  
pp. 50-59 ◽  
Author(s):  
Manuel Chica ◽  
Pascual Campoy
Keyword(s):  
Computer Vision ◽  
Classification Techniques ◽  
Bee Pollen ◽  
Pollen Loads ◽  
One Class Classification

Bee pollen loads and their use in indicating flowering in the Caatinga region of Brazil

2010 ◽  
Vol 74 (10) ◽  
pp. 1355-1358 ◽  
Author(s):  
Jaílson Santos de Novais ◽  
Luciene Cristina Lima e Lima ◽  
Francisco de Assis Ribeiro dos Santos
Keyword(s):  
Bee Pollen ◽  
Pollen Loads

scholarly journals Influence of the Collection Season on Production, Size, and Chemical Composition of Bee Pollen Produced by Apis Mellifera L.

2014 ◽  
Vol 58 (2) ◽  
pp. 5-10 ◽  
Author(s):  
Adriana F. Negrão ◽  
Lidia M. R. C. Barreto ◽  
Ricardo O. Orsi
Keyword(s):  
Particle Size ◽  
Apis Mellifera ◽  
Chemical Composition ◽  
Protein Content ◽  
Nutritional Quality ◽  
Particle Sizes ◽  
Bee Pollen ◽  
Pollen Loads ◽  
Food Scarcity ◽  
Collection Period

Abstract The aim of our study was to investigate how the collection period affects and influences the production, chemical composition, and size of bee pollen loads (0.5, 1.0, 2.0, greater than 2.0 mm). The results showed there was a predominance of pollen loads with a diameter greater than 2.0 mm in all the production seasons. For all the seasons, there were no differences in protein content between the particle sizes. But when comparing 0.5 mm during the different periods, there were significant differences; the highest value was found during the winter (24.39 ± 3.7%). As far as lipids and crude fiber are concerned, we obtained differences between the same granulometry sizes for the spring and summer seasons. As for ashes, the results showed differences between different particle sizes for the summer and autumn seasons. Our results have shown that regardless of pollen particle size, its quality was not altered, suggesting that smaller loads can be commercially used by containing nutritional quality or else be used by beekeepers as a supplement during periods of food scarcity.

An Analysis of Honey Bee Pollen Loads from Banthra, Lucknow, India

Grana ◽  
1973 ◽  
Vol 13 (3) ◽  
pp. 139-144 ◽  
Author(s):  
Mithilesh Chaturvedi
Keyword(s):  
Honey Bee ◽  
Bee Pollen ◽  
Pollen Loads

scholarly journals Botanical profile of bee pollen from the southern coastal region of Bahia, Brazil

2010 ◽  
Vol 24 (3) ◽  
pp. 862-867 ◽  
Author(s):  
Marcos da Costa Dórea ◽  
Jaílson Santos de Novais ◽  
Francisco de Assis Ribeiro dos Santos
Keyword(s):  
Apis Mellifera ◽  
Plant Species ◽  
Coastal Region ◽  
Botanical Origin ◽  
Mimosa Pudica ◽  
Bee Pollen ◽  
Pollen Loads ◽  
Pollen Types ◽  
Pollen Sources ◽  
Forest Biome

This paper aims to identify the botanical origin of pollen loads collected by Apis mellifera L. in Canavieiras municipality, Bahia state. It provides a list of polliniferous plant species from the Atlantic Forest biome that are important for the development of regional apiculture. Using the acetolysis method, 35 bee-pollen samples were analyzed qualitatively and quantitatively. Results showed that pollen types Elaeis (23.99%), Mimosa pudica (22.78%) and Cecropia (13.68%) were the most abundant among the samples. These also showed the highest relative frequencies of the material studied and were important pollen sources for bees in the study area.

scholarly journals MAPPING OF BURNED AREA USING PRESENCE AND BACKGROUND LEARNING FRAMEWORK ON THE GOOGLE EARTH ENGINE PLATFORM

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 37-41
Author(s):  
D. Attaf ◽  
K. Djerriri ◽  
D. Mansour ◽  
D. Hamdadou
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Google Earth ◽  
The Other ◽  
Burned Area ◽  
Support Vector ◽  
Classification Techniques ◽  
Burned Areas ◽  
Google Earth Engine ◽  
One Class Classification

<p><strong>Abstract.</strong> Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.</p>

scholarly journals Morphological, Physicochemical and FTIR Spectroscopic Properties of Bee Pollen Loads from Different Botanical Origin

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3974 ◽  
Author(s):  
Sara Castiglioni ◽  
Paola Astolfi ◽  
Carla Conti ◽  
Elga Monaci ◽  
Mariassunta Stefano ◽  
...  
Keyword(s):  
Spectroscopic Properties ◽  
Botanical Origin ◽  
Spectra Analysis ◽  
Bee Pollen ◽  
Pollen Loads ◽  
Visual Identification ◽  
Pollen Content ◽  
Bee Foraging ◽  
Typical Composition

Bee pollen loads generally have a homogeneous and monospecific pollen content and assume a typical form and color, due to the typical bee foraging habits, thus having a typical composition related to the botanical origin. The present study aims to characterize bee pollen loads belonging to different botanical species using morphological, spectroscopic and color properties and to find relationships between these variables. IR spectra analysis allowed to have a reliable picture of the components present in the different samples; color and granulometry permits a visual identification of pollen load belonging to different species. Multivariate analysis enabled differentiation among the botanical origin of most of the bee pollen samples, grouping them according to the family and the genus and confirming the possibility to use IR and color measurements for the evaluative analysis and classification of bee pollen samples, to promote the consumption of this bee product as functional food.

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