Enabling Data Collection and Analysis for Precision Agriculture in Smart Farms

Abstract

This article presents an in-depth exploration of multifaceted efforts in agricultural research aimed at addressing the unpredictable nature of crop production and related processes, including the demonstration of data collection and its application. This research focuses on leveraging current technologies and devising sustainable solutions to mitigate uncertainties attributed to natural climatic conditions and infectious agents. The central theme of this review centers around the utilization of Internet of things sensors for data collection, cloud software for data processing, and the integration of diverse machine learning algorithms for data analysis. The objective is to advance insights into the application of these technologies in agriculture and their potential to enhance crop yield and sustainability. The article comprehensively explores the technological landscape and the levels at which current research is being conducted, shedding light on machine learning algorithms, their specific functions, and comparative analysis of each algorithm based on different use cases. Furthermore, the article presents an implementation approach that integrates sensors and machine learning. Its primary application is to predict the type of crop to produce based on nutrient levels detected by the sensors.