In recent years, the agricultural sector has been rapidly transformed by the use of advanced technologies, often referred to as 'smart farming' or 'smart agriculture'. Digital tools and other scientific and technological developments have been widely used to revolutionise agricultural practices for better productivity and sustainability. Detecting water stress in plants is one of these challenges to be addressed. Typically, soil moisture sensors are used to assess the condition of the crop. However, the state of the art has identified multispectral imagery as a promising method for detecting water stress in crops, especially using near-infrared (NIR) and red-edge bands. This study investigates the use of multispectral imagery that doesn't only use RGB channels but also NIR and red-edge channels to improve the detection of water stress in potato crops using advanced object detection models.