Automatic 3D Pollen Recognition Based on Convolutional Neural Network

Abstract

The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken into account. Therefore, this work focuses on a three-part novel approach: constructing spatial local key points to obtain local stable points of pollen images, computing orientational local binary pattern using local stable points as the inputs, and identifying the pollen grains using convolutional neural network as the classifier. Experiments are performed on two standard pollen image datasets: Confocal-E dataset and Pollenmonitor dataset. It is concluded that the proposed approach can effectively extract the features of pollen images and is robust to posture transformation, slight occlusion, and pollution.

A user‐friendly method to get automated pollen analysis from environmental samples

“…Our study is based on purpose on simple tools and thus on images from slides scanned under light microscopy. Other types of pollen data have been tested for the automation of pollen analysis, and gave good classification performances, by‐passing some limitations encountered in our study: pollen images acquired with flux cytometry (Dunker et al ., 2021; Barnes et al ., 2023), with scanning electric microscopy (Li et al ., 2023), or with confocal microscopy then classified with a 3D‐classification algorithm (Wang et al ., 2021).…”

Using the FA-NAR Dynamic Neural Network Model and Big Data to Monitor Dam Safety

“…Some scholars have done a lot of useful work in this field of exploration and achieved certain results [2]; XingLi, ZhipingWen, HuaizhiSu, and so forth proposed random forest intelligent algorithms to build dam safety monitoring model methods [3]; Wang Lirong, Zheng Dongjian, and so forth proposed to use CNN to identify dam safety monitoring data anomaly modes in order to reduce the data processing pressure of dam safety monitoring data anomaly identification and solve the problem that traditional methods find it difficult to identify non-maximum abnormal points [4]; SiyuChen, ChongshiGu, and so forth used RBF neural networks and kernel main component analysis to establish a safety monitoring model for ultra-high concrete dams [5]; FeiKang, AMASCE, and JunjieLi proposed a Gaussian process regression model for the health monitoring of concrete gravity dams [6]; BoDai et al proposed to use statistical models and random forest regression (RFR) models to predict the deformation of concrete dams [7]; Dowrueng A, Thongthamchart C, Raphitphan N, and so forth describe decision-making based on the civil engineering expertise of the dam safety remote monitoring system: DS-RMS, which can make action-based recommendations based on everyday scenarios and special events such as earthquakes and floods. The key benefits include quick and reliable access to current information about the dam and relief for dam managers in critical situations.…”