Smart Agriculture: IoT and Machine Learning for Crop Monitoring and Precision Farming
Keywords:
Smart Agriculture, IoT, Machine learning, Crop monitoring, precision farmingAbstract
Through the implementation of cutting-edge technology like the Internet of Things (IoT) and machine learning, smart agriculture, which is also referred to as precision agriculture, is bringing about a revolution in the conventional agricultural techniques that have been used for generations. The purpose of this article is to present an overview of how the Internet of Things (IoT) and machine learning are utilised in crop monitoring and precision farming in order to improve production, maximise resource usage, and reduce environmental consequences. The Internet of Things (IoT) devices that are connected with sensors are placed throughout agricultural fields in order to collect real-time data on a variety of environmental characteristics. These parameters include soil moisture, temperature, humidity, and nutrient levels. These sensors are connected to one another over wireless networks, which enables the transmission of data to centralised cloud-based platforms for statistical analysis in a smooth manner. In order to recognise patterns, correlations, and anomalies in the data that has been collected, machine learning algorithms are applied to the subject matter. The development of predictive models allows for the forecasting of agricultural yields, outbreaks of pests and diseases, and the implementation of ideal irrigation schedules. In order to enable farmers to make educated decisions about irrigation, fertilisation, pesticide application, and crop management methods, decision support systems offer them with recommendations and alerts that may be put into action. The report also discusses the Internet of Things (IoT) and machine learning for crop monitoring. In addition to that, challenges associated with precision farming are discussed in this research.
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