Optimizing Rice Yield Quality Through Advanced Machine Learning Techniques in Precision Farming

Abstract: The continuously rising global trend in demographics is a challenge that modern agriculture has to contend with to feed the world by producing food produced with scarce inputs. In response, Advanced emerging technologies like machine learning (ML) and the IoT have birthed intelligent farming. This paper gives a decision-making framework for crop management, particularly using machine learning models to identify rice quality. Apache Kafka and Spark are used with the system and are coupled with big data techniques for enhanced data streaming and processing. Real-time classification of rice quality using various ML algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Feedforward Neural Networks (FFNN) are used. This research shows that this methodology significantly improves the decisionmaking approach in precision agriculture as a viable framework for future agricultural systems. Lastly, suggestions for future development aret presented regarding incorporating environmental information and utilizing the models in wider scouting practices.