Insect pest identification is very important for greenhouse management. Having the knowledge of what insects exist in their greenhouse, farmers will be able to determine which pesticide will be more effective to prevent insect pest outbreaks and protect their crops. The most common technique to monitor insect pests is the use of strips of yellow sticky papers. Insects trapped on these yellow sticky papers are usually counted by human inspection without the assistance of any machine or device. To replace this inefficient method, this work presents a multi-class insect identification method for yellow sticky paper, obtained from wireless cameras using cascaded convolutional neural networks (CNN). The designed algorithm makes use of a marker-based image segmentation technique for object detection. The objects are sorted using an insect vs. non-insect filter CNN model to remove non-insect objects such as glare, dirt, and water droplets with 88-95% counting accuracy, while the multi-class insect classifier has an accuracy of 86-92%. The CNN models are optimized based on accuracy and computation time for real-time insect pest monitoring application. The combined algorithm can process each yellow sticky paper image with an average processing time of 13-15 seconds and 2-3 seconds using a quad-core Cortex A53 1.2GHz CPU and GTX1080 2.2GHz GPU, respectively. This work can be applied for real-time and remote insect pest monitoring using wireless camera networks and for observing insect population dynamics of different species.