Technological revolution in farming has been led by advances in sensing technologies. Nowadays the ability of applying the state of the art related to Internet of Things (IoT) is intensely increasing however, the development of daily long-distance agricultural systems is still in its early stage. As agricultural sector continues to be suffering with climate changes, the current challenges of the less favorable climatic conditions thrives the greater risks of transboundary plant pests and diseases; which affect crops production, as well as threatening food security and some significant losses to the farmers. In this research we have combined the sensors devices using wireless sensors networks(WSN), to build a farmland environmental monitoring platform that can simultaneously monitor eight important environmental parameters identified as high correlation to boom pests and diseases in plantation. The overall structure of the system enabled real-time monitoring and acquisition of the huge amount of data on daily basis. Due to this reason, we have researched insight of these collected data using machine learning technique through the algorithms like KNN, Random Forest, Logistic Regression and Linear Regression. The objective of this paper is to do an experiment on benefit of using IoT system in farmland for data collection and analysis for identifying a prediction model which can be used for predicting outbreaks of plantation diseases with better accuracy.