This study focuses on designing an Internet of Things (IoT) based remote greenhouse pest monitoring system using wireless imaging and sensor nodes (WiSN). The system designed can continuously monitor the number of pest insects detected on yellow sticky papers distributed in multiple locations and can measure the environmental parameters simultaneously. The pest insects are counted through image processing and machine learning algorithms in which it can be classified into black and white objects specifically white flies and fruit flies with an average accuracy of 98% and computation time of 8-9 seconds per image. Data collection was done at a greenhouse in National Taiwan University Experimental Farm in which cabbages were grown as main crop. The greenhouse, without using pesticides, is occasionally infested by white flies and fruit flies. Furthermore, the data are processed by the server in which the real time analysis and monitoring are shown in a monitoring website that also includes the latest acquired yellow sticky paper images as well as the data plots. The feasibility of the system was tested, and the experimental results show that light intensity has the highest correlation with the pest insect count. The developed system is effective to acquire pest count accurately and automatically which provides important spatial-temporal information that allows for efficient integrated pest management in greenhouse operations.