Zooplankton is a key ecological component of aquatic ecosystems. Studying and monitoring the spatial distribution and temporal variability of zooplankton is vital to understanding their community composition and its relation to climate change. Manual methods for analysis are time-consuming and have limitations on the ecological studies of these organisms. Real-time, fast and accurate in situ zooplankton detection and classification remains a challenge. Currently, research focuses on automating zooplankton image classification using handcrafted computer vision techniques and neural network based approaches [1,2]. Most recent approaches adopt deep learning techniques for identification and classification [3,4]. In this paper, we propose the use of Fast Region-based Convolutional Neural Network (Faster R-CNN) for fast and accurate in situ detection and classification of zooplankton groups in underwater images. Faster R-CNN is a region-based object detection framework which combines region proposal and classification in a unified network [5]. Indeed, end-to-end learning reduces overall training time and increases the accuracy of the network. Faster R-CNN has shown state-of-the-art performance on benchmarks such as ImageNet and VOC [6,7]. We perform the experiments over ZooScan, Kaggle, WHOI-Plankton datasets [8,9,10]. We evaluate the performance of our proposed approach of in situ zooplankton identification and classification in terms of detection speed and mean Average Precision (mAP). In addition, we compare the performance of the proposed method with popular detectors such as Single Shot Multibox Detector (SSD) and You Only Look Once (YOLOv3) to demonstrate its efficacy at processing noisy underwater images [11,12]. Results of our evaluation recommend the use of Faster R-CNN for real-time zooplankton image analysis. The ultimate goal of this work is to automate the current manual process exerted in the lab while improving the accuracy and processing speed of an otherwise time-consuming task for marine biologists.