Smart Sensing based Functional Control for Reducing Uncertainties in Agricultural Farm Data Analysis

Smart farming is a promising application area that relies on diverse intelligent and communication technologies to ease the outcome of the farming process. The multi-level processes in a farming scenario are automated through smart sensors and intelligent decision-making system. This article presents a smart sensor information processing method for controlling the farming devices' functions. This method is named smart sensing-based functional control (SSFC) that is devised to identify and mitigate the uncertainties in handling information. Uncertainties in information processing are addressed using Boltzmann machines (BM) with limited and effective layered processing. Based on the recommendations of the BM output, the dissemination of device controls is preceded. In particular, both analysis and control dissemination are filtered based on BM constraints and training sets, improving the devices' accuracy. With the help of experimental analysis and real-time data set, the performance of the proposed SSFC is investigated using the metrics analysis time, analyzed rate, dissemination delay, uncertain controls, and accuracy. From the investigation, it is seen that the proposed SSFC achieves 13.2% analysis time, 6.27% high analyzed rate, 18.15% less dissemination delay, 22.04% uncertain control, and 7.17% accuracy.