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Analysis of Smart Sensors Nodes for Decision Making and Classification in Irrigation System
Amin Salih Mohammed

Artificial Intelligence (AI) has been considered as a booming approach that can be applied to huge amount of sensing environment for realization of prediction, recognition management and controlling tasks. Moreover, it merges with embedded system to offer effectual outcome, however encounters certain drawbacks. Here, a low power sensing system is anticipated with AI, WSN and deep learning based on-boards with special consideration towards agriculture applications with smart sensors. For this purpose, Convolutional Neural Network (CNN) with Fuzzy decision is modelled to attain effectual accuracy and finest Intersection over Union (IoU) parameter in collected sensor data and irrigation data from sensor placed over agriculture land for validating collected dataset. The anticipated solution is capable to carry out irrigation period recognition, feature detection for performing irrigation through processing collected data. For CNN training, collected data for irrigation purpose at diverse stages are validated. The complete system is evaluated in farming land. Experimental outcomes depict that anticipated system depicts extensive vista for smart agriculture application in context to smart sensors necessitating autonomous ad intelligent factors from nodes. The anticipated model includes regression to offer better trade off in contrary to prevailing approaches. It works superiorly with Performance metrics like IoU.

Keywords: Smart sensors, embedded sensing, artificial intelligence, machine learning, smart agriculture, CNN, Intersection over Union

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