PHENOBIID: Rethinking In-Season Crop Yield Prediction from a World Model Perspective

Crop yield is the terminal outcome of a dynamic growth process, where weather drives vegetation evolution and accumulated seasonal development determines harvest productivity. However, during the growing season, only part of this process is observable before harvest, making it challenging to predict yield from incomplete growth information. Existing in-season yield prediction methods mainly aggregate available observations and directly regress final yield, leaving the hidden evolution of vegetation dynamics insufficiently modeled. In this work, we reformulate in-season yield prediction as a predictive world-modeling problem and introduce PhenoBIID, a task-specific weather-conditioned vegetation world model that learns weather-driven remote sensing vegetation state transitions and predicts hidden growth trajectories. Instead of directly mapping partial seasonal observations to yield, PhenoBIID reconstructs the unobserved portion of vegetation development and uses the completed trajectory for yield estimation, explicitly connecting environmental forcing, vegetation dynamics, and crop outcomes. To evaluate this perspective, we introduce CropDynamicsBench, a global benchmark covering maize, rice, soybean, and wheat that jointly evaluates vegetation trajectory forecasting and yield prediction under varying observation availability. In retrospective evaluations under realized weather, PhenoBIID reduces seed-mean yield RMSE by 0.12–4.74% relative to the strongest evaluated direct baseline at each crop–cutoff setting with 10–70% unobserved windows. Our findings highlight the potential of predictive world modeling to connect crop growth dynamics with yield assessment and support agricultural planning. Code, processed benchmark inputs, an executable inference sample, and evaluation artifacts are available at https://anonymous.4open.science/r/PhenoBIID-5EBB