Utilizing an Internet of Things (IoT) Device, Intelligent Control Design, and Simulation for an Agricultural System

Smart agriculture systems can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. To date, agriculture systems have utilized different technologies such as precision farming, hydroponics, aquaponics, robots, temperature and moisture sensors, aerial images, GPS technology, and vertical farming [6]. The most popular applications of artificial intelligence in the agriculture industry are in three major categories: agricultural robots, predictive analytics, and crop and soil monitoring. Computer vision and deep-learning algorithms are used to process data captured by drones and/or software-based technology to monitor crops. Farmers use technology daily. Automated drones already monitor fields and collect data on crops. Agricultural robots are being developed to carry out the fieldwork. Robots have successfully planted, tended, and harvested crops. In order to realize the full potential of the IoT, there is a need to integrate ubiquitous smart devices and cloud-based applications [7,8]; a combined IoT framework with a cloud at the center gives the flexibility of dividing associated costs in the most logical manner and is also highly scalable. In the combined framework, sensing service providers can join the network and offer their data using a storage cloud; analytic tool developers can provide their software tools; artificial intelligence experts can provide their data mining and machine learning tools useful in converting information to knowledge; and computer graphics designers can offer a variety of visualization tools [9,10,11,12]. An agricultural management method supported by technology monitors, assesses, and evaluates the requirements of specific fields and crops. The focus of these efforts is on robotics, which includes sensors, aerial images, and sophisticated local weather forecasts, as well as Big Data and advanced analytics capabilities. This results in reduced environmental impact, financial savings, and optimized fertilizer utilization. Using data and information technologies to optimize intricate farming systems is known as smart farming. Smart farming does not place as much emphasis on exact measuring or differentiating between or within individual animals, as it does with PA. The emphasis is more on data application and access—that is, how to make intelligent use of the information gathered. Farmers can obtain real-time data regarding the state of the soil and plants, the terrain, the climate, the weather, the utilization of resources, labor, and funding [13,14]. By using mobile devices like smartphones and tablets, farmers now possess the knowledge necessary to base their judgments on factual information rather than gut feeling. Regular use of web-based data platforms in conjunction with Big Data analysis, internal and external farm networking, and precision and smart farming techniques is common. Products for IoT-enabled smart agriculture are made to automate irrigation systems and use sensors to monitor crop areas. Consequently, farmers and related brands may conveniently and remotely check agricultural conditions. Agriculture currently uses a variety of technologies. Therefore, to determine the degree of technology employed in the system, it is necessary to identify and compare the various technologies. For simplicity, this is compared in Table 1.

Researchers are using a variety of technologies in agriculture systems, including machine learning, remote sensing, image processing, sensor networks, and so on, to boost agricultural productivity. In order to gain insights, it is necessary to compare the controller algorithms used in agricultural systems. The comparison is based on how easily it can be automatically tuned during operation, as demonstrated in Table 2.