Algae Recognition and Usage Prediction Using Deep Convolutional Neural Network

Algae species are essential to many environmental processes, such as the sequestration of carbon, the creation of oxygen, and the cycling of nutrients. Precise identification and forecasting of algae species are crucial for comprehending the dynamics of ecosystems, evaluating the quality of water, and creating sustainable solutions like the manufacturing of biofuel. However, conventional techniques for classifying algae are frequently labor-intensive and prone to human mistake. In response, a unique method that uses deep convolutional neural networks (CNNs) to automate the procedure presented in this study. Through the process of selecting and organizing a wide range of algal picture datasets and the application of data augmentation techniques, an attempt to improve the model's capacity to generalize to other species and environmental circumstances. With the help of the deep CNN architecture, an attempt to accurately identify and forecast species from photographs of algae by extracting complex patterns and traits. Through extensive experimentation, this method performs better than traditional methods, underscoring its potential to transform algae-related research and applications in environmental science and other fields.