Lettuce is an annual plant of the family Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds, and its growth status and quality are evaluated based on its morphological phenotypic traits. However, traditional measurement methods are often labor-intensive and time-consuming due to manual measurements and may result in less accuracy. In this study, we proposed a new method utilizing RGB images and Mask R-Convolutional Neural Network (CNN) for estimating lettuce critical phenotypic traits. Leveraging publicly available datasets, we employed an improved Mask R-CNN model to perform a phenotypic analysis of lettuce images. This allowed us to estimate five phenotypic traits simultaneously, which include fresh weight, dry weight, plant height, canopy diameter, and leaf area. The enhanced Mask R-CNN model involved two key aspects: (1) replacing the backbone network from ResNet to RepVGG to enhance computational efficiency and performance; (2) adding phenotypic branches and constructing a multi-task regression model to achieve end-to-end estimation of lettuce phenotypic traits. Experimental results demonstrated that the present method achieved high accuracy and stable results in lettuce image segmentation, detection, and phenotypic estimation tasks, with APs for detection and segmentation being 0.8684 and 0.8803, respectively. Additionally, the R² values for the five phenotypic traits are 0.96, 0.9596, 0.9329, 0.9136, and 0.9592, with corresponding mean absolute percentage errors (MAPEs) of 0.1072, 0.1522, 0.0757, 0.0548, and 0.0899, respectively. This study presents a novel technical advancement based on digital knowledge for phenotypic analysis and evaluation of lettuce quality, which could lay the foundation for artificial intelligence expiation in fresh vegetable production.

Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R² of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.

The lack of a rapid, non-destructive method for accurately determining ginseng seed quality remains a significant bottleneck for industrial-scale nursery production and quality control. The post-germination seeds are minuscule and exhibit subtle morphological variations, making automated determination particularly challenging. To address this, this study proposes YOLO-GS, an improved deep learning model based on YOLOv11n. YOLO-GS incorporates a suite of dedicated attention-enhanced modules (including SELP, CPCA, AssemFormer, and C2PSA_CloFormerAttnConv) that work synergistically to significantly enhance the network's ability to capture fine-grained features and subtle defects. On a self-built ginseng seed dataset, YOLO-GS achieves a precision of 96.7%, an mAP@0.5 of 97.7%, and an F1-Score of 96.5%, outperforming the baseline YOLOv11n with minimal computational overhead (4.26 M parameters, 14.1 GFLOPs). When deployed on a Jetson edge device, the model achieves an inference speed of 0.6 ms per image. This demonstrates its strong potential for enabling large-scale, automated sorting systems based on real-time quality determination, thereby enhancing efficiency and standardization in ginseng seed nursery production.