Forecasting the Number of Harvest-ready Fruits of Sweet Peppers Using Multimodal Time-Series Data

Accurate yield forecasting at the individual-plant level is critical for precision agriculture and supply-chain planning, yet public datasets capturing both visual growth dynamics and per-plant measurement labels are scarce. In this paper, we introduce a novel, annotated image time-series dataset of 691 sweet pepper plants monitored over two growing seasons, comprising 4837 images with per-plant fruit counts categorized by maturity. We propose a multimodal deep learning framework that fuses high-dimensional image features, extracted using the DinoV3 encoder, with numerical count measurements. Our architecture utilizes a Long Short-Term Memory (LSTM) network to model temporal dependencies and handles irregular sampling intervals common in greenhouse monitoring. Through quantitative experiments, we demonstrate that this multimodal approach reduces RMSE over a persistence baseline by 33