With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitefly (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring.