Authors - Mike Philip T. Ramos, Andres R. Vicedo, Jocelyn O. Padallan, Jonardo R. Asor, Genemarck B. Catedrilla Abstract - This research aims to develop a model for plankton species classification by analyzing images utilizing a convolutional neural network or CNN to simplify the task of classifying plankton species. The use of CNN and other transfer learning models will be used to recognize different freshwater plankton species in order to identify the genus of plankton easily. There were several layers in the CNN architecture used in this study; (1) Layer 1 has convolutional data with 32 filters and 3x3 kernel with max pool of 2x2 kernel; (2) Layer 2 has convolutional data with 64 filters and 3x3 kernel with max pool of 2x2 kernel; and (3) Layer 3 has conventional data with 128 filters and 3x3 kernel with mas pool of 2x2 kernel. After the validation and training in terms of accuracy and loss for CNN and pre-trained models, it is observed that MobileNetv2 showed the highest positive scores with 0.99 in train accuracy, 0.93 in validation accuracy, 0.07 in train loss, and 0.12 in validation loss, which makes it more viable to be used in this study. CNN's capacity to extract characteristics from photos has shown to be highly effective at classifying images. Additionally, it has been determined that transfer learning strategies can aid CNN in enhancing its picture categorization capabilities. The use of pre-trained learning like MobileNetv2 with a small data set and image classification studies can be a greater help for identification than CNN, Convolutional Network, Rest- Net50 and EfficientNetB0
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