Agriculture productivity has a significant impact on a nation's economy, and crop disease detection in early stage is crucial in this area. Plant quality and quantity of production suffer if the disease is not quickly identified. Delayed detection of plant disease is the main reason for less production of vegetables and fruits. Farmers can benefit a lot if disease related information is provided to them at the right time. Hence, the biggest obstacle to increased agricultural productivity is the accurate and prompt identification of infections in plants. Traditional detection methods are inaccurate, time-consuming, expensive, and subjective. Many intelligent solutions have been put forth by researchers for the automatic identification of plant leaf infections and for overcoming the limitations of the manual technique. In this research work, an IoT RGB sensor based approach is discussed to monitor plant health condition by analysing leaf images. The information gathered from IoT sensors is examined to identify potential plant infections by NSS image processing mechanism. This combined IoT and image processing approach is proved as a success and yields good results with an accuracy of 99.21% on 5 different types of leaves on publicly available dataset.