Karpagalakshmi, R. C. (26428205700); Rajesh Sharma, R. (56604200600); Kumar, Kesanapalli Dileep (60058504000); Masote, Prashant (60057579600); Kumar, M. Jayanth (60058656300); Reddy, Chada Santhosh (60057896900)R. C. (26428205700); Rajesh Sharma, R. (56604200600); Kumar, Kesanapalli Dileep (60058504000); Masote, Prashant (60057579600); Kumar, M. Jayanth (60058656300); Reddy, Chada Santhosh (60057896900)Karpagalakshmi2026-02-052026-02-0520259798331521516https://dx.doi.org/10.1109/ICIMA64861.2025.11073984https://www.scopus.com/pages/publications/105013851270https://gnanaganga.alliance.edu.in/handle/123456789/8669Precision agriculture has become an essential practice for optimizing crop yield and resource utilization. This research integrates classification and regression models to enhance crop prediction and yield estimation. The study employs a Random Forest Classifier for crop recommendation and utilizes regression models such as Linear Regression, Decision Tree Regressor, and K-Neighbors Regressor for yield estimation. Feature Importance Analysis is also applied to identify key factors influencing crop growth. The proposed hybrid approach aims to improve prediction accuracy, scalability, and practical applicability, providing a comprehensive decision-support system for farmers. © 2025 IEEE.enCrop Prediction; Feature Importance Analysis; Machine Learning; Precision Agriculture; Random Forest; Regression Models; Yield EstimationConference paper