Internet of Things and Machine Learning for Smart-Agriculture: Technologies, Practices, and Future Direction
Keywords:Agricultural sensors, Smart Agriculture, Wireless technologies, Internet of things, Machine learning
Smart agriculture has quickly gained popularity due to the tremendous introduction, growth, and integration of modern techniques with conventional agriculture, including the internet of things (IoT), computer vision (CV), machine learning (ML), big data, edge computing, and cloud computing. With the use of inexpensive sensors, smart agriculture aims to improve the effectiveness and sustainability of agriculture. These comprise airflow, location, optical, and mechanical sensors. Along with the real-time monitoring, identification, and categorization of objects, these sensors can be used to gather information about the position of crops and assess the condition of the soil. Furthermore, because IoT and open wireless communication networks are used in smart-agriculture ecosystems, these channels are vulnerable to a variety of cyber threats and security concerns. It has been stated that these actions could have a significant negative impact on the economy of a wide range of nations due to the rise in harmful assaults on the agricultural industry. Without the necessity for a centralized authority, AI and blockchain can be utilized to address a number of difficulties related to the deployment and management of smart agriculture. The benefits of cloud computing include its capacity to manage enormous amounts of data while offering a range of storage alternatives. Edge computing, on the contrary hand, provides a faster response time and less latency. The technique smart agriculture systems are created is anticipated to change as a result of this interaction. As a consequence, the way these techniques are applied is changing paradigmatically.
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