K-Means Clustering Technique to Increase Production in Floriculture Farming using IOT

Floriculture has become an important commercial activity in agriculture sector in the post globalization era. Floriculture activity has marked as a viable and profitable trade area with a potential to activate self-employment among low and middle income farmers, and earn the very essential foreign exchange in the developing countries such as India. The production and export of floricultural products have received a considerable interest in recent decades from the researchers, policy makers, agricultural and horticultural experts. Increasing the productivity is an important factor. To do this we analyze the climate conditions and the soil situation about the water level in the soil is an important factor for producing more yield of flower. This paper proposes the Random Tree method to deal with both classification and regression problems for flower production and used K-means clustering for a given soil data the soil physical factors are grouped using this algorithm and find out the result. Dataset were collected with the help of the IOT using random generation of result of dataset to find in various time of the climate change and provide a result for jasmine flower yield.