Enhancing Smart Agricultural Management by Using Big Data Analysis and IoT

Abstract

The objective of the present investigation is to improve agricultural practises by incorporating cutting-edge sensor technologies and data analytics. A thorough system is created that combines a temperature sensor, a humidity sensor, a leaf sensor, a GPS module, and an image sensor to systematically capture crucial data on a particular crop inside an agricultural domain. These various data sets are smoothly delivered to the launch pad of the CC3200 and are afterwards archived in a cloud infrastructure. The collected data is effectively shared with users, in this case farmers, by an Android application that accesses and displays the data from a cloud repository. The application of strong decision-making and predictive algorithms enhances the efficacy of this integrated system further. To handle and analyse the data saved in the cloud, these algorithms make use of artificial neural networks (ANN), fuzzy logic, and neuro-fuzzy systems. The Android application may provide informative recommendations and forecasts thanks to the cooperative use of different computational techniques. The proposed solution greatly aids farmers in making well-informed decisions by utilising this combination of data-driven methodologies.