Accurate plant counting supports stand assessment and crop management, but canopy overlap, camera motion, and temporary occlusion complicate video-based monitoring. This study developed an RGB image- and video-based framework for detecting, tracking, and counting cabbage seedlings and grown plants under greenhouse and open-field conditions. The framework integrated YOLOv8n with convolutional block attention modules (CBAM) and an improved BoT-SORT procedure incorporating trajectory persistence, short-gap relinking, class-consistent identity management, and counting-event filtering. Detector performance was evaluated across five independent training runs, counting performance across nine held-out videos, and tracking performance on four manually annotated videos. YOLOv8n-CBAM achieved mean precision, recall, mAP@0.50, and mAP@0.50:0.95 values of 0.880 ± 0.007, 0.920 ± 0.007, 0.960 ± 0.006, and 0.600 ± 0.008, respectively (mean ± sample standard deviation). The complete framework achieved 99.27% mean counting accuracy, a mean absolute error of 0.56 plants, a root mean square error of 0.88 plants, and an R² of 0.997. On the annotated subset, Improved BoT-SORT increased mean IDF1 from 79.6% to 82.3% and mean HOTA from 74.3% to 83.2%, while reducing total identity switches from 23 to 12. Complete-pipeline throughput was 23.38 frames per second on an RTX 3090 workstation. These findings support cabbage population monitoring under the evaluated conditions; independent-site validation, tracking-component ablation, and deployment-device testing remain necessary to establish broader applicability.

Warm-season greenhouse crops such as tomato (Solanum lycopersicum L.) and paprika (Capsicum annuum L.) require nutrient supply strategies that respond dynamically to temperature, solar radiation, vapor pressure deficit (VPD), root zone water status, and electrical conductivity (EC). This systematic review assessed four operational strategies: (1) solar radiation-proportional irrigation; (2) radiation-rate-based volume and EC adjustment; (3) supply–drainage and substrate-feedback control; and (4) temperature, humidity, and VPD-based supervisory correction. Scopus, Web of Science, IEEE Xplore, ScienceDirect, and MDPI were searched on 15 August 2026 for studies published from 2010 onward, yielding 4088 records (2326 after removal of duplicates). Studies were eligible if they addressed a controlled-environment production system, a warm-season crop, a nutrient-management component, and the use of sensing, automated control, models or decision-support systems, or IoT/AI tools. After title/abstract screening and full-text assessment, 90 studies were included: 29 studies from the database search and 61 studies identified by manual searching of publisher platforms, the three other references were from textbooks. Radiation-based approaches provide practical feedforward estimation but incompletely represent humidity and root zone dynamics. Supply–drainage feedback offers stronger closed-loop information, while temperature and VPD-based correction improves responsiveness to atmospheric demand. Tomato requires careful calcium and potassium management during reproductive stages, whereas paprika demands stable substrate moisture, EC, and drainage. A conceptual hierarchical framework is proposed in which solar radiation establishes the baseline nutrient-solution supply, radiation-rate information provides short-term adjustment, root-zone and drainage measurements provide feedback correction, and temperature or VPD serves as a supervisory layer. Ion-specific sensing, mechanistic models, and AI/ML approaches can further support nutrient-specific and predictive management. Few included studies evaluated these strategies under explicitly imposed high-temperature conditions; therefore crop, cultivar, and growth-stage-specific validation under commercial high-temperature greenhouse conditions is required before operational thresholds can be generalized.