Profiling the Arion rufus snails with computer vision

Smart agriculture takes advantage of modern computational approaches that vary from IoT, cloud computing, and artificial intelligence. The primary aim is to assist the farming process. Pest detection is one of the objectives within the area of smart agriculture. It is mainly solved by computer vision approaches, usually combined with machine learning (ML) algorithms. In this paper, we propose a solution for detecting Arion rufus snails that have emerged in Central Europe and are one of the most prolific threats to agriculture in that place. Practical experiments reveal that our method is helpful in this real-world application and opens several future challenges and lines of research.