A Short-Term Yield Prediction Method for Greenhouse Strawberries Integrating Visual Phenology and Meteorological Sequences

Highly perishable strawberries demand strict post-harvest time management, making accurate short-term yield prediction central to optimizing modern greenhouse production and supply chain scheduling. However, existing models that rely excessively on isolated environmental factors exhibit delayed responsiveness to actual crop physiological dynamics and struggle with integrating multimodal data. To overcome these limitations, we propose a short-term method for predicting greenhouse strawberry yield that integrates visual phenology with meteorological sequences. The proposed method was validated using a multimodal dataset acquired from 150 tracked greenhouse strawberry plants over a 72-day monitoring period (11 December 2025, to 20 February 2026), incorporating continuous microclimate records and an image repository of 784 original images annotated into five distinct phenological classes (flower, green, white, pink, and red). First, using our improved YOLO11-SC model, we effectively resolve challenges of complex illumination and dense foliage occlusion, achieving high-precision automated extraction of five consecutive strawberry phenological stages. Second, by fusing these visual markers with meteorological time series (e.g., temperature, humidity, and light intensity), we construct a multimodal spatiotemporal feature matrix. To accommodate diverse smart agriculture application scenarios, we designed two distinct prediction architectures: on servers with ample computing power, a Bidirectional Temporal Convolutional Network with self-attention (BiTCN-SA) to achieve highly accurate predictions; and for resource-constrained IoT edge nodes, a lightweight machine learning ensemble (Stack-LGR). Experimental results demonstrate that, in predicting the cumulative mature fruit yield within the next harvesting cycle, BiTCN-SA achieves strong performance with a coefficient of determination (R2) of 0.958 and a root mean square error (RMSE) of 3.154. Simultaneously, the edge-deployed Stack-LGR ensemble maintains stable prediction accuracy (R2 = 0.892) while ensuring acceptable inference latency. This study mitigates the latency limitations of single-environment-driven models. It provides a solution for precise crop yield prediction and tiered computational deployment, with good predictive performance, deployment adaptability, and methodological reference value.