A critical review on hybrid framework for precise farming with application of Machine Learning (ML) and Internet of Things (IoT)

Abstract

Precise Farming, commonly referred to as site-specific crop management, is the use of technology to increase agricultural   output and efficiency. Due to the availability of real-time data and insights on crop growth, soil quality, weather patterns, and other crucial elements, the integration of machine learning (ML) and the internet of things (IoT) has completely changed the way farming is done. This review paper focuses on the creation and application of a hybrid IoT and ML system for precise farming. The ML algorithms can process enormous amounts of data and produce insights that can assist farmers in making defensible decisions regarding their farming methods. The framework's IoT devices are in charge of gathering data from diverse sources and transmitting it to a central system for processing. Due to the hybrid nature of the framework, several technologies can be combined to produce a cohesive and effective system for precise farming. By combining ML and IoT, it is possible to use fewer pesticides and fertilizers, increase crop yields, and use less water. The framework is useful for usage in large-scale farming operations due to its adaptability and scalability. In conclusion, the hybrid framework for precise farming that applies ML and IoT is a promising technology that can aid farmers in increasing their output and efficiency while lessening their impact on the environment. Further investigation is required to evaluate its efficacy and identify any implementation difficulties.

URN:NBN:sciencein.jist.2024.v12.730