The Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Suhas Kakade (Department of Electrical Engineering COEP Tech. University, Pune, India.)
Rohan Kulkarni (Department of Electrical Engineering, KJCOEMR, Pune, Maharashtra, India)
Somesh Dhawale (Department of Electrical Engineering COEP Tech. University, Pune, Maharashtra, India)
Muhammed Fasil C (Department of Applied Electronics and Instrumentation Engineering, GEC Kozhikode, Kerala, India)

Green Intelligent Systems and Applications
Volume 3 - Issue 2 - 2023

Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.