Paper Title
Analysis of Machine Learning Techniques for Smart Agriculture: A Comprehensive Survey

Abstract
Agriculture sector is a very crucial sector in terms of both economy and livelihood. It is one among the significant contributor to global GDP which is about 4.6% till 2021 and it has been rising gradually in following years. Even though it is an effective sector to eradicate poverty there is no effectiveness in following traditional agricultural methods in modern times. With the advancement in technology during recent times integration agriculture to technological aspects had brought a significant improvement in the effectiveness of this sector. Smart agriculture integrates advanced technologies to optimize farming practices. This survey explores the role of Machine Learning (ML) in transforming traditional agriculture into intelligent systems. We use ML techniques such as Clustering, Regression, Classification based on their applications-crop monitoring, yield prediction, disease detection, and resource optimization and analyze their effectiveness, limitations, and future potential. Keywords - Machine Learning, KNN (K Nearest Neighbors), Neural Network, Support Vector Machine (SVM), Principal Component Analysis (PCA), Performance, Accuracy, Classification.