Scheduling Method for Agricultural IOT Business Based on Improved Multiobjective Evolutionary Algorithm

Abstract

In the modern society where technology is advancing every day, the agricultural industry is also undergoing innovation, and the Internet of Things (IoT) based on machine learning algorithms adds new vitality and yields increasing directions to this ancient industry. This study analyzes and processes data based on improved multiobjective algorithms for the application of IoT in agriculture and establishes the relevant algorithmic models. The components of IoT are introduced, and it is determined that information flow, capital flow, logistics, and Internet are the main reasons why it can be generated. After establishing an improved multiobjective evolutionary algorithm model with good convergence and diversity, the embedded multichannel sensor collection device measured in this experiment in the same cultivated environment has a more stable collection data cycle compared to the external sensor. The embedded multichannel sensor has better stability, so this sensor is selected for this study to monitor parameters such as soil moisture content and oxygen content. The IoT requires timely communication and consultation among users, and the actual experiment found that the use of ultrashort waves with a frequency of 230 MHz is the most stable and efficient.