A low-cost and low-burden secure solution to track small-scale fisheries

During the last decade accurate spatial and quantitative information of industrial fisheries have been increasingly given using tracking technologies and machine learning analytical algorithms. However, in most small-scale fisheries, lack of spatial data has been a recurrent bottleneck as Vessel Monitoring System and Automatic Identification System, developed for vessels longer than 12 and 15 m in length respectively, have little applicability in these contexts. It follows that small-scale vessels (< 12 m in length) remain untracked and largely unregulated, even though they account for most of the fishing fleet in operation in the Mediterranean Sea. As such, the tracking of small-scale fleets tends to require the use of novel and low cost solutions that could be addressed by small vessels often without dedicated electrical systems. In this paper we propose a scalable architecture that makes use of a low-cost LoRaWAN/cellular network to acquire and process positioning data from small-scale vessels; preliminary results of a first installation of the prototype are presented, as well as the data collected. The emergence of a such low-cost and open source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management, and cross-border marine spatial planning.