Abstract: Traditional grape management relies heavily on empirical judgment, resulting in strong subjectivity in decision-making and a lack of quantitative phenotypic perception and reliable early warning evidence for timely canopy, water, and fertilizer management. To address these problems, this study proposed a grape management early warning method based on the collaborative identification of multidimensional key phenotypes. First, according to the practical agronomic requirements of grape canopy management and water and fertilizer regulation, berry shape, cluster shape, and cluster compactness were selected as three key phenotypic indicators for management early warning. A collaborative in situ identification system integrating deep learning and statistical analysis was then developed to achieve quantitative characterization of these indicators under field conditions. A convolutional block attention module (CBAM) was integrated into the berry oriented detection and cluster segmentation networks, and geometric parameters were extracted based on the detection bounding boxes and segmentation masks to achieve oriented berry detection, cluster segmentation, and the determination of berry and cluster shapes under field conditions. For more detail, berry length, width, and related geometric parameters were calculated from the oriented bounding boxes to support berry shape determination; cluster length, width, and related geometric parameters were calculated from the segmentation masks to support cluster shape determination. In addition, based on the geometric features extracted from grape clusters, a partial least squares discriminant analysis (PLS-DA) model was established to achieve interpretable and quantitative grading of cluster compactness. On this basis, a multi-indicator weighted fusion strategy was adopted to construct the management early warning model. The outputs of berry shape, cluster shape, and cluster compactness identification were quantified and integrated according to their relative importance, thereby realizing a quantitative mapping from key phenotype identification results to prioritized management requirement categories, including no management required, canopy management, water and fertilizer management, and integrated improvement. Experimental results showed that the proposed collaborative identification system achieved reliable performance under field conditions. The mean average precision at an intersection over union threshold of 0.50 (mAP₅₀) for berry oriented detection and grape cluster segmentation reached 85.7% and 96.5%, respectively. The agreement rates between the model outputs and manual reference labels were 87.0% for berry shape, 92.0% for cluster shape, and 90.3% for cluster compactness. When the three key phenotypes were evaluated jointly, the joint identification agreement rate for the three phenotypes was 78.7%, indicating that the system could provide integrated phenotypic information for subsequent management early warning. For the management early warning model, the overall classification agreement reached 86.2%, while the agreement rate for samples requiring management was 84.1%. The macro-averaged F1-score was 85.7%, and Cohen’s kappa coefficient was 0.814, demonstrating a high level of agreement between the model outputs and manual reference labels after correction for chance agreement. These results indicate that the proposed method can effectively integrate multidimensional phenotypic information and distinguish among the priority management categories in the current validation set. The proposed method establishes a complete technical workflow from phenotypic sensing and indicator quantification to early warning output. It provides a quantitative basis for identifying priority management needs in grape production and offers an engineering reference for the development of intelligent systems for precision grape management.