In this work we use drip irrigation where the water was allowed to drip slowly to the roots of plant either from above the soil surface or buried into the surface so that the water can be placed directly into the root zone and minimize evaporation. It uses temperature sensor, soil humidity sensor to collect and monitor field information and also uses float switches to monitor ground water level through web page. When the field gets dry and ground water level falls down it will be notified through SMS. This provides a solution for the problems in developing a smart farming system. It uses node MCU, relay and water pump.

The fundamental tools to discover knowledge from big data was matrix composition. Here data generated by modern applications via cloud computing. However, it is still inefficient or infeasible to process very big data using such a method in a single machine or through virtual machines. Moreover, big data are often distributedly collected data from various data centers and stored on different machines via scheduling algorithms. Thus, such data generally bear strong heterogeneous noise. It is essential and useful to develop distributed matrix decomposition for big data analytics. Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system. To this end, we propose a Distributed Bayesian Matrix Decomposition model (DBMD) for big data mining and clustering. Specifically, we adopt three strategies to implement the distributed computing including (1) the accelerated gradient descent, (2) the alternating direction method of multipliers (ADMM), and (3) the statistical inference. We investigate the theoretical convergence behaviors of these algorithms. To address the heterogeneity of the noise, we propose an optimal plug-in weighted average that reduces the variance of the estimation. Finally, comparison made between these algorithms to understand the result between them.