In controlled environment agriculture (CEA), accurate yield forecasting remains challenging due to reliance on environmental sensor data, which fails to capture plants’ dynamic morphological responses to growth conditions. This study bridges the gap by establishing a vision-based framework to forecast plant growth dynamics through automated phenotyping and time-series modeling. A plant phenotype monitoring framework was implemented using commercially available cameras and off-the-shelf deep learning-based models (YOLO). The robustness of the YOLO and time-series models was evaluated under various treatment conditions, including salt stress and variations in root architecture, in hydroponic greenhouse trials across two seasons. Top-view images of the plants were collected using GoPro and Raspberry Pi cameras, and different YOLOv8 instance segmentation model variants were trained on four image datasets to extraction of morphological traits such as area, major, and minor axes. Results indicated that YOLOv8 generalized well, achieving mAP50 for bounding boxes and masks in the range of 0.897 – 0.952 and 0.896 – 0.947, respectively. Plants with split root systems exhibited superior growth under the highest salt stress levels compared to single-root systems. Comparisons between physical measurements and image-derived parameters such as major and minor axes yielded high R² values of 0.85 and 0.92 for single-root systems, and 0.90 and 0.84 for split root systems. Additionally, the area parameter obtained from images showed an R² of 0.882 when compared with plant fresh weight. Area parameters were forecasted using an ARIMA model over 2-, 4-, and 8-day windows, evaluated using MAPE. The lowest MAPE values (3.99 in the fall and 1.70 in the spring) were attained by single-root plants under salt stress when projected for two days. The forecasted area values demonstrated R² values of 0.623, 0.671, and 0.75 for the 2-, 4-, and 8-day forecast windows respectively when compared with fresh weight, indicating that the area parameter is a reliable predictor of yield. These findings confirm that morphological changes capture environmental influences and can be reliably forecasted, introducing a scalable, data-driven method to predict yield in CEA while helping growers optimize resource usage and reduce productivity risks.
