Non-Destructive Sensing and Modeling for Biomass Estimation and Yield Prediction of Protected Vegetables: A Review

Accurate acquisition of biomass and yield information for protected vegetable crops is essential for crop growth assessment, environmental regulation, optimal harvest timing, and production planning. With advances in machine vision, spectral sensing, and artificial intelligence technologies, research in this field is shifting from destructive sampling and single-time-point estimation toward non-contact, multisource, continuous monitoring and dynamic prediction. Focusing on protected leafy and fruit vegetables, this review summarizes biomass and yield indicators and their ground-truth measurement methods and compares the characteristics of RGB imaging, three-dimensional vision, spectral sensing, and environmental data. It then reviews advances in biomass estimation, continuous growth monitoring, and harvest prediction for leafy vegetables, as well as flower and fruit sensing, fruit counting, individual fruit mass estimation, and stage-specific harvest yield prediction for fruit vegetables. Current research is expanding from static estimation at the individual-plant level to growth-process monitoring and stage-specific yield prediction, but challenges remain, including interference from complex environments, difficulties in the spatiotemporal alignment of multisource data, incomplete continuous information, and limited model adaptability. Future research should strengthen robust sensing under complex conditions, dynamic multisource fusion, and mechanistic–data-driven integration, thereby advancing the field from individual-plant estimation toward dynamic monitoring of cultivation units and production decision support.