Hybrid Machine Learning Framework for Agricultural Data Collection and Preprocessing for Crop Yield Prediction

Abstract Objectives: The aim is to develop a combined Machine learning architecture for proficient agricultural data collection and preprocessing to equip accurate prediction of crop yield and relatable permit assessment. The designed work standardizes heterogeneous datasets, integrates robust preprocessing techniques to corroborate data authenticity and consistency, identifies the influential agronomic and environmental factors and in addition regularization mechanisms are included to maintain model generalization, prevent overfitting and delivers a scalable end-to-end solution for agricultural analytics and governance. Methods: To provide all the components to the corresponding range, the methodology executes Min-Max integration, z-score irregularity extract, and K-Nearest Neighbours (KNN)-based estimation utilising a crop yield dataset accompanying specialized atmospheric details. Findings: Pursuant to a comparison study, the data's firmness and integrity have substantially developed, with irregularity instance reduced by beyond 80% and absent benefit reduced by exceeding 90%. When evaluating an anti-specific system, the fore preparation data elevate exactness to 96.8% and reduce RMSE to 0.039, thrashing many new baselines. Novelty: These findings validate that compatible precautioning is a main factor of agricultural evaluation and a resilient basis for the advanced hybrid patterning that was introduced in the other paper. Keywords: Agricultural informatics, Data preprocessing, KNN imputation, Crop yield prediction, Deep learning