Monitoring System for Increasing Crop Yield Production

Abstract

For humanity to survive, agriculture is by far the most crucial aspect. Low-yield crops are currently causing farmers problems. It might result in a food shortage. Low crop yield is mostly caused by a lack of understanding of soil fertility and crop choices. The same crop is chosen each season, which reduces the fertility of the land. The key to enhancing agricultural output is choosing the right crop, which can be done by doing a soil study and taking into account metrological parameters. This paper aims to generate smart farming and lower agricultural risk. This system depends on a monitoring system that uses IoT devices to gather data, and a machine learning algorithm to analyze the data once it has been extracted in real time. The data is collected that is pertinent to the plant using a number of sensors, and then we further analyze it using machine learning algorithms. Numerous methods, including KNN, Decision Tree, Random Forest, Gaussian Naive Bayes, and Extreme Gradient Boosting, are utilized for crop analysis. It can forecast the best crop to plant in order to increase yield production with the use of IoT and machine learning technology.