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

Agave flower visitation by pallid bats, Antrozous pallidus, in the Big Bend region of Texas

Pallid bats, Antrozous pallidus, though primarily insectivorous gleaning predators, are known to consume nectar of the cardón cactus, Pachycereus pringlei, in the Sonoran Desert. It is unknown whether a similar nectar feeding behavior may be occurring in the Big Bend region of Texas, where several researchers have captured pallid bats covered in pollen. I collected pollen samples from 67 pallid bats in Brewster County, Texas between April and August 2018. Pollen-covered pallid bats were captured in every month sampled. The pollen collected in all samples was homogeneous and identified as Agave pollen. Two species of Agave occur in this region of Texas, Agave havardiana and Agave lechuguilla. A linear discriminant analysis classified 556 of 723 of the pollen grains analyzed as A. lechuguilla. Additional evidence from infrared video footage collected in August of 2018, indicates that pallid bats are becoming covered in A. lechuguilla pollen as a result of nectarivory