In-season crop yield prediction: State of the art and future research direction

• Systematic review of 55 studies (2014–2025) on in-season crop yield prediction. • Compared machine learning, process-based, and statistical models across scales. • Identified Random Forest, CNN, and LSTM as the most commonly used methods. • Reviewed key inputs including NDVI, EVI, LAI, weather, and soil variables. Reviewed key input variables, including vegetation indices (NDVI, EVI, LAI), weather, and soil-related parameters, and summarized their roles in prediction performance. • Summarized challenges and future needs for nonlinear, data-fusion, scalable models. Synthesized major challenges, including model interpretability, data availability, scale transferability, and climate variability. Accurate in-season crop yield prediction is important for farm management, insurance planning, and food security decision making. Many studies have proposed different models and data sources, but their findings are difficult to compare because they focus on different crops, regions, spatial scales, prediction months, and evaluation metrics. This review presents a structured synthesis of in-season crop yield prediction studies and to identify key gaps that limit early and reliable prediction. We conducted a systematic literature review following a defined protocol. We searched multiple databases using targeted search strings, and screened studies using clear inclusion and exclusion criteria. Eligible studies focused on corn, soybean, or winter wheat, were published between 2014 and 2025, and predicted crop yield during the growing season. Review papers were excluded. This process identified 170 studies, of which 55 were retained for detailed analysis. For each selected study, we extracted information on modeling approach, input data sources, prediction lead time, spatial scale, and reported evaluation metrics. We synthesized results across three main model types, machine learning models, process-based crop models, and statistical models. This review addresses five research questions: (1) What modeling approaches are most commonly used for in-season crop yield prediction, and how do their predictive performances compare? (2) What input data sources and feature types are most frequently used, and how do they influence prediction accuracy? (3) What prediction lead times are most commonly used for major crops, and how does lead time affect model performance? (4) What are the main challenges and limitations in current in-season crop yield prediction studies? (5) What methodological and data-driven strategies can improve future in-season crop yield predictions? The reviewed literature shows that no single approach consistently outperforms others across all conditions. Model performance depends strongly on data quality, crop type, and regional conditions, with common limitations related to data gaps, cloud contamination in satellite imagery, and limited model interpretability.