IoT and Machine Learning Approaches for Classification in Smart Farming

Abstract

Precision farming is the future of conventional farming. The digitization of farming has led to the development of machine learning (ML) systems in a variety of administrative areas so that more may be gained from the vast quantities of data now available. Knowledge-based farming systems present a number of obstacles, but one area of artificial intelligence with great potential is machine learning & Deep Learning. The development of a number of cutting-edge technologies has made this possible. The idea of this article is to encourage smart farming practices and lessen agricultural risks. While agricultural progress is nothing new, the wireless sensor powered by artificial intelligence will usher in a new era of precision farming. The focus of this study is on developing new machine learning methods for better prediction. Machine learning in agriculture, however, doesn't appear to be in sync with the field's central research. The difficulties already present in agriculture data only serve to exacerbate the situation. The effects of these data problems on several machine learning techniques are also investigated, and applied to the field of agriculture. We have looked at the naive bayes and KNN classification algorithms for precision agriculture in this paper. We have analyzed the data and determined the optimal classification method for use in precision agriculture by taking into account a wide range of factors.