Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system)

Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination. Our experimental results indicate that different pollen types often have different light scattering characteristics, as previous research has suggested. Our results also show that light scattering distributions did not undergo significant temporal changes. Further, we show that the concentration of two different types of pollen could be estimated separately from the total number of pollen grains by fitting the light scattering data to a probability density curve. These findings should help realize a fast and simple automatic pollen monitoring system.

Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – Establishment of Automated Multi-taxa Pollen Counting Estimation System (AME System)

Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination. Our experimental results indicate that different pollen types often have different light scattering characteristics, as previous research has suggested. Our results also show that light scattering distributions did not undergo significant temporal changes. Further, we show that the concentration of two different types of pollen could be estimated separately from the total number of pollen grains by fitting the light scattering data to a probability density curve. These findings should help realize a fast and simple automatic pollen monitoring system.

An improved pollen number counting method using a cell counter and mesh columns

Background The determination of pollen number is important in evolutionary, agricultural, and medical studies. Tree species of the Cupressaceae family cause serious pollinosis worldwide. Although Japanese cedar (Cryptomeria japonica) is the most important forestry species in Japan, it is also the biggest cause of pollinosis in the country. Japanese cedar trees have been selected for growth speed and superior morphological traits and then cloned. These clones may vary in their pollen production, but there has been little research on how many pollen grains are produced by a single male strobilus (flower). A recently reported method for counting pollen number with a cell counter was applicable to Arabidopsis species and wheat, but was not suitable for Japanese cedar because the strobilus does not open with heating (e.g. 60 °C, overnight). Results Here, we report an improved pollen counting method for Japanese cedar using a precise and rapid cell counter in combination with home-made mesh columns. The male strobilus was gently crushed using a pestle. Large and small debris were then removed using 100- and 20-μm mesh columns, respectively. We successfully detected pollen sizes and numbers that differed between two clones using this method. Conclusions This improved method is not only suitable for counting pollen from Japanese cedar, but could also be applied to other species of the Cupressaceae family with hard scale tissue covering the pollen. Moreover, this method could be applied to a broader range of plant species, such as wheat, because there is no need to wait for anthesis and debris can be removed efficiently.

An improved pollen number counting method using a cell counter and mesh columns

BackgroundThe determination of pollen number is important in evolutionary, agricultural, and medical studies. Tree species of the Cupressaceae family cause serious pollinosis worldwide. Although Japanese cedar (Cryptomeria japonica) is the most important forestry species in Japan, it is also the biggest cause of pollinosis in the country. Japanese cedar trees have been selected for growth speed and superior morphological traits and then cloned. These clones may vary in their pollen production, but there has been little research on how many pollen grains are produced by a single male strobilus (flower). A recently reported method for counting pollen number with a cell counter was applicable to Arabidopsis species and wheat, but was not suitable for Japanese cedar because the strobilus does not open with heating (e.g. 60°C, overnight). ResultsHere, we report an improved pollen counting method for Japanese cedar using a precise and rapid cell counter in combination with home-made mesh columns. The male strobilus was gently crushed using a pestle. Large and small debris were then removed using 100- and 20-μm mesh columns. We successfully detected pollen sizes and numbers that differed between two clones using this method. ConclusionsThis improved method is not only suitable for counting pollen from Japanese cedar, but could also be applied to other species of the Cupressaceae family with hard scale tissue covering the pollen. Moreover, this method could be applied to a broader range of plant species, such as wheat, because there is no need to wait for anthesis and debris can be removed efficiently.

An improved pollen number counting method using a cell counter and mesh columns

Background The determination of pollen number is important in evolutionary, agricultural, and medical studies. Tree species of the Cupressaceae family cause serious pollinosis worldwide. Although Japanese cedar (Cryptomeria japonica) is the most important forestry species in Japan, it is also the biggest cause of pollinosis in the country. Japanese cedar trees have been selected for growth speed and superior morphological traits and then cloned. These clones may vary in their pollen production, but there has been little research on how many pollen grains are produced by a single male strobilus (flower). A recently reported method for counting pollen number with a cell counter was applicable to Arabidopsis species and wheat, but was not suitable for Japanese cedar because the strobilus does not open with heating. Results Here, we report an improved pollen counting method for Japanese cedar using a precise and rapid cell counter in combination with home-made mesh columns. The male strobilus was gently crushed using a pestle. Large and small debris were then removed using 100- and 20-µm mesh columns. We successfully detected pollen sizes and numbers that differed between two clones using this method. Conclusions This improved method is not only suitable for counting pollen from Japanese cedar, but could also be applied to other species of the Cupressaceae family with hard scale tissue covering the pollen. Moreover, this method could be applied to a broader range of plant species, such as wheat, because there is no need to wait for anthesis and debris can be removed efficiently.

Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques

The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains is a handicap due to the high complexity of the images to be processed, with polymorphic and clumped pollen grains, dust, or debris. The purpose of this study is to analyze the feasibility of implementing a reliable pollen grain detection system based on a convolutional neural network architecture, which will be used later as a critical part of an automated pollen concentration estimation system. We used a training set of 251 videos to train our system. As the videos record the process of focusing the samples, this system makes use of the 3D information presented by several focal planes. Besides, a separate set of 135 videos (containing 1234 pollen grains of 11 pollen types) was used to evaluate detection performance. The results are promising in detection (98.54% of recall and 99.75% of precision) and location accuracy (0.89 IoU as the average value). These results suggest that this technique can provide a reliable basis for the development of an automated pollen counting system.