A Clustered Feature Representation Model for Predicting Crop Health and Optimizing Yield

Machine learning applications' revolutionization of data processing and decision-making techniques has several implications for the global economy. Due to the increased need for food worldwide, agriculture is being significantly impacted. This research examines the benefits of combining machine learning algorithms with modern agriculture. Through deliberate decisions on agricultural production, these algorithms maximize crop yield while minimizing waste. To provide outcomes with great accuracy, this paper describes the use of a machine clustering mechanism for optimizing crop yield. The study includes collecting extensive data about the farm, merging online data received in real-time, and collecting data from the farmers about the verdicts that affect the crops. After analyzing fifteen distinct algorithms, the best method is determined, and an upgraded algorithm with a new feature scheme is awarded. This investigation infers a 98% classification accuracy using a clustering technique. Robust infrastructure and comfortable surroundings result from higher output rates and lower effective farming expenses.