IoT and Machine Learning-Based Smart Agriculture: A Review

The rapidly developing field of "smart agriculture" leverages artificial intelligence and machine learning to enhance productivity, sustainability, also efficient in agricultural area. However, numerous obstacles to agricultural productivity exist, including diverse soil types, varying climates, and limited access to modern farming methods. This study examines current methods in smart agriculture with machine learning technique and Internet of Things suited to the agricultural environment. Large amount of data gathered from different sources are analysed using artificial intelligence and machine learning algorithms, and IoT-enabled soil testing is enabled with sensors. Furthermore, artificial intelligence and machine learning -powered virtual assistants can provide tailored advice on smart agricultural techniques, helping farmers make informed decisions and mitigate risks. Numerous approaches, including machine learning, image processing, numerical weather prediction, weather research forecasting, and others, have been proposed in this context, utilizing techniques such as genetic algorithms, neural networks, support vector machines, fuzzy logic, k-means clustering, k-nearest neighbor, and rough sets. This review paper surveys several research papers on smart agriculture systems that utilize ML and the IoT for smart farming. Suggestions regarding the direction of future research are also enclosed with this paper.