A Deep Learning Approach for Classification of Pollen Grains using Proposed CNN Model

Pollen grains are microscopic structures produced by plants in order to reproduce. These grains are necessary for the pollination and fertilization processes, which are vital for the continued existence and diversification of plant species. Botanists and scholars have been interested in the identification and categorization of pollen grains for many years. A convolutional neural network (CNN) model is proposed in this study for the classification of pollen grains from a dataset of 805 photos with 23 annotated classes and an image resolution of 128 × 128 pixels with 3 color channels. The images of pollen grains from 23 plant species make up the publicly accessible Pollen Grain Dataset, which provided the dataset for this study. Based on the physical traits and properties of the pollen grains, the suggested CNN model is intended to properly categorize the pollen grains. The performance of the suggested CNN model is evaluated using various performance metrics. The CNN model’s categorization of pollen grains had an accuracy rate of 87.60%. The outcomes of this study’s investigation show how well CNN models can categorize pollen grains. Compared to manual categorization, which may be error-prone and time-consuming, this automated technique can save a lot of time and effort. According to the observations, the suggested CNN model is efficient at classifying pollen grains and may be applied to several tasks, including recognizing diverse pollen species for use in environmental study, agricultural practice, and scientific inquiry.