**3.5 Onboard processing**

Our proposed approach targets the implementation of the monitoring functionalities on board, instead of collecting measurements from the site and post-processing them offline afterward. This will ease the monitoring and most importantly will allow the quick assessment of the spill area, to take the best measures and actions toward the possible damage. For this reason, it is very important to check the feasibility of implementing the proposed approach onboard, especially since the platform that we are suggesting is the drone, which is a battery-powered device where the energy and power consumption are of high importance. We show the feasibility of our approach for onboard processing by selecting one of the previously described algorithms and implementing it on a hardware platform, namely the Pynq Z1 Field Programmable Gate Array (FPGA). While machine learning algorithms are often considered highpower consuming and introduce a high complexity in terms of memory footprint and computational requirements, we select the ANN model that performs the estimation and classification functionalities for the implementation. It is also good to note that including "0 mm" as one class for the regression estimation is equivalent to including the detection functionality. Therefore, we believe that the implementation complexity of this model is very significant since the latter can perform all the main required functionalities for oil-spill monitoring at the same time. Results show that, based on the architecture used during the implementation, only 1.13–28.37% of the available resources are utilized by the network. Similarly, the power consumption varies between 34 and 133 mW, which is negligible compared with the power drawn by

drones in the order of 10th of watts. This verifies the feasibility of our ANN-based approach and demonstrates the suitability for a practical scenario.
