Impact Statement:Internet of Things (IoT) in agriculture has the potential to completely transform the industry by enabling more streamlined and effective operations. Sensors based on the...Show More
Abstract:
As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern comm...Show MoreMetadata
Impact Statement:
Internet of Things (IoT) in agriculture has the potential to completely transform the industry by enabling more streamlined and effective operations. Sensors based on the IoT, such as temperature sensors, light sensors, pressure sensors, moisture sensors, and others enable the automation and simplification of a wide range of trustworthy user-oriented information, such as high-quality data, documented vulnerabilities, and appropriate measurement using artificial intelligence (AI). Artificial intelligence of things (AIoT) aims to improve data management and analytics while increasing the efficiency of IoT operations. Furthermore, smart agriculture operations necessitate a solid understanding of local weather conditions, soil quality, crop monitoring, and preventive measures. The article highlights recent research (2019–2023) on ML approaches (a subset of AI approaches) and their prospective applications in smart agriculture. The article serves a number of purposes. It serves as a referen...
Abstract:
As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create int...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 5, Issue: 6, June 2024)