Navigating Data Quality in Agricultural Analytics

This chapter explores the challenges of maintaining data quality in agricultural analytics, focusing on issues like noise and missing values. It emphasizes the need for reproducibility and transparency in data modeling, weighing the pros and cons of explainable models versus black box approaches. Additionally, the chapter discusses the complexities of agricultural data forecasting, highlighting the importance of incorporating domain knowledge to improve predictive accuracy and model performance.