## 1 Introduction
Current citizen science validation protocols are labour-intensive and subject to potential human errors, such that citizen scientists do not receive prompt feedback to their submitted reports, leading to a possible disengagement of the same contributors. On the pro side, current protocols are characterized by a high degree of taxonomic precision, such that the rate of misidentification is generally very low. In this work, the hundreds of photos submitted to the \"Spot the Jellyfish\" initiative for the five most commonly recorded species within Maltese waters (_Pelagia noctiluca_, _Cotylorhiza tuberculata_, _Carybdea marsupialis_, _Veella veella_ and salps) are used to train and evaluate the performance of a tailored-designed, region-based Convolution Neural Network (R-CNN). The main aim of the developed digital architecture is to identify correctly the jellyfish species recorded in the submitted photo, thus eliminating or reducing the need for human expert intervention and providing a prompt feedback to the citizen scientist. Semiquantitative estimates of jellyfish individual abundance will also be generated by the algorithm when presented with photos of jellyfish blooms. A region-based method is applied to identify and record the jellyfish sighting and the confidence level for the model's taxonomic identification output. Over the last decade, R-CNNs have been researched and successfully applied to a wide-range of applications including the detection of tumours [13], the detection of ships [14], the detection of debris on the airfield pavement [15], to give an early warning of sharks in coastal areas through the use of drones [16], for traffic sign recognition [17], to detect three-dimensional objects during autonomous driving [18], as well as for the classification of cracks in concrete [19]. The most basic form of R-CNN makes use of an external algorithm such as the Edge Boxes method to come up with region proposals [20]. Pixels corresponding to the identified regions are then extracted, resized and processed via a support vector machine (SVM) that is trained using CNN features [21]. The Fast R-CNN implementation makes use of the Edge Box algorithm to identify regionsthat might contain an object of interest [22]. Although the CNN features of the identified regions are extracted, the results coming from overlapping parts are shared. While this makes the process faster, the entire images still need to be processed. In the Faster R-CNN method, a Region Proposal Network (RPN) is embedded within the detector to eliminate the need of an external algorithm to identify important sub-areas [23]. Apart from making the implementation faster, this allows the detection of regions to be attuned to the dataset itself. RPNs are able to identify valid boxes of any size quickly, by making use of anchor boxes. In particular, processing is carried out within a set of predefined boxes, with dimensions that correspond to the objects that are to be identified. This eliminates the need to perform computations on every possible position of a sliding window. In this work, the anchor boxes allow the definition of the expected aspect ratios of image regions that show a jellyfish. The classification models then try to fit objects within the given ratios. This approach allows for a better performance to be obtained when multiple jellyfish cover each other, such as in the case of a bloom. Faster R-CNNs are designed to give quick and accurate results, even when a large number of regions are defined. Being a deep learning method, features are extracted from the provided data automatically. Throughout the supervised learning process, filters in the hidden layers are modified so as to extract important features and minimise the prediction error. All development in this study was done using Matlab by Mathworks ([[https://www.mathworks.com](https://www.mathworks.com)]([https://www.mathworks.com](https://www.mathworks.com))). In particular, use of the Vision toolbox was made to label and augment the dataset, as well as to train the models.