Zonghua Leng

The growing demand for rapid and nondestructive sensing in precision and smart agriculture, particularly for continuous crop monitoring in controlled environment agriculture (CEA), has stimulated interest in terahertz (THz) technologies. Owing to their sensitivity to water, low-frequency molecular vibrations, and dielectric properties, THz waves provide a promising approach for probing agricultural materials. This review summarizes advances in THz sensing for agricultural engineering. The principles and characteristics of THz time-domain spectroscopy, frequency-domain spectroscopy, and imaging are first introduced, together with the major interaction mechanisms between THz waves and agricultural materials. Advances in THz data analysis are then discussed, focusing on model selection, reliability under small-sample conditions, multi-source information fusion, and mechanism-informed analysis. Applications are subsequently reviewed in seed quality and germplasm identification, soil property assessment and contaminant detection, crop water-status monitoring, stress and disease detection, and agricultural product quality and safety assessment. Key challenges include complex-matrix interference, weak target responses, limited model generalization across scenarios, and system integration. Future research should emphasize high-sensitivity sensing, mechanism-data-integrated modeling, and miniaturized online detection systems to facilitate the practical deployment of THz technologies in agriculture, including greenhouses and other controlled production environments.

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.

Protected horticultural crops are commonly produced at high planting densities and have short production cycles; imbalances in water and fertilizer supply can rapidly affect plant vigor, yield, and quality. Non-destructive diagnostic methods are therefore needed to characterize plant nutritional status under greenhouse conditions. Spectral imaging can simultaneously capture spatial and spectral information associated with pigments, water status, tissue structure, and canopy phenotype. It does not directly detect nutrient ions; rather, it captures physiological and structural responses that may be associated with nutrient status and may also be influenced by water deficit, disease, temperature, salinity, phenology, and genotype. This review focuses on crops grown in soil, substrate, and hydroponic systems under greenhouse conditions. Studies conducted in vertical farms, growth chambers, and open fields are included only as supplementary references for sensor selection, model calibration, and inspection methods. This article synthesizes diagnostic indicators for nitrogen, phosphorus, and potassium, together with their associated physiological responses and spectral characteristics; compares the performance of hyperspectral, multispectral, and machine learning methods at the leaf, plant, and canopy scales; and examines fixed measurement, stop-and-go mobile inspection, continuous motion imaging, and autonomous plant revisitation. Existing studies have established a solid foundation for nutrient content retrieval, deficiency identification, and mobile monitoring. However, several challenges remain inadequately addressed under continuous inspection conditions, including radiometric–geometric joint calibration, plant identity preservation, acquisition of multi-element chemical truth values, model generalization across growth stages and greenhouse types, and long-term performance evaluation. Future work should refine standardized protocols for dynamic data collection and water–fertilizer environmental control, integrate mechanistic constraints with data driven approaches, and incorporate plant re-identification, spatiotemporal registration, uncertainty quantification, and online calibration. These efforts will contribute to constructing a long-term stable and comparable nutritional diagnostic system, thereby advancing the transition of facility vegetable nutritional monitoring from single-time static measurements toward continuous, traceable, and autonomously patrolled systems that may ultimately support precision irrigation and fertilization management after appropriate independent validation.

The production of facility vegetables is of great significance but there are still limitations to this production in terms of yield and quality. Optical sensing technology offers a rapid and non-destructive solution for phenotypic analysis, which is superior to traditional destructive methods. This article reviews and analyzes nine optical sensing technologies, including RGB imaging, and introduces the application of various algorithms in combination with detection principles throughout the entire growth cycle as well as key phenotypic characteristics of facility vegetables. Each technology has its advantages. For example, RGB and multi/high-spectrum technologies are the most frequently used while thermal imaging is particularly suitable for early detection of non-biological and biological stress responses, and these technologies can effectively obtain physiological, biochemical, yield, and quality information about crops. However, current research mainly focuses on laboratory verification and there is still a significant gap when it comes to practical production. Future progress will depend on the integration of multiple sensing technologies, data analysis based on artificial intelligence, and improvements in model interpretability. These developments will be crucial for ultimately achieving precise breeding and intelligent greenhouse management systems, and will gradually transition from basic phenotypic analysis to comprehensive decision support systems.