In this paper, we present a case study on the transition to informed automated decision-making processes in smart agriculture. Our focus is on addressing the challenges posed by the new invasive global pest, Halyomorpha halys (HH), which causes significant economic damage to fruit orchards. Specifically, we aim to automate the time- and labor-intensive process of HH scouting, which is traditionally performed by phytosanitary operators. Our objective is to demonstrate the pipeline of technological and methodological decisions necessary for automating the scouting process. To gather images from the orchard, we utilized a drone equipped with an RGB camera as well as other devices such as smartphones. Despite the suboptimal quality of the images captured by the drone’s camera, our computer vision algorithm for HH detection yields promising results. These findings serve as an encouragement to further explore the possibilities of technology transfer to the agriculture.