In our previous study, we developed a monitoring system for automatically counting tomatoes produced in protected horticulture using deep learning–based object detection. In this study, we adapted the system for sweet peppers and developed a monitoring system tailored to this crop. We evaluated its fruit detection and counting performance in a large-scale commercial greenhouse. Furthermore, we investigated the relationship between fruit counts at different ripeness stages and the total yield in the cultivation area, and we assessed the accuracy when predicting the yield for the following week. The results confirmed that the system maintained a stable fruit detection performance throughout the trial, and that its outputs were reliable enough to indicate its potential to replace manual counting. In addition, the average number of fruits at the 1–40% and 41–80% ripeness stages across six planting rows showed a correlation with the total weekly yield in the entire 0.6 ha cultivation area the following week. A yield prediction model using average fruit counts at these two ripeness stages as explanatory variables achieved a WAPE of 21.35%, indicating that the monitoring system is effective for yield prediction.

Fruit instance segmentation models are widely researched for inference tasks such as yield prediction and automated harvesting. Previous studies evaluated these models only on the basis of mask average precision; they overlooked segmentation quality and confidence score reliability—both crucial for inference tasks. This study proposes an evaluation method that incorporates, in addition to mask average precision, the Aggregated Jaccard Index to assess segmentation quality and the coefficient of determination between confidence scores and Intersection over Union for reliability evaluation. We compared YOLO11, Mask R-CNN, and their improved variants using a dataset that included stable and comprehensive imaging obtained by monitoring equipment in a large-scale commercial paprika greenhouse. Results show that mask scoring R-CNN excels in segmentation quality, while YOLO11 performs better in mask average precision and confidence score reliability. These findings suggest that when evaluating instance segmentation models for real-world application scenarios, we should not rely solely on mask average precision, but a combination of multiple metrics that assess different aspects of the model must be utilized.