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Aiming to address the challenges of low automation and poor accuracy in traditional field wheat phenotyping data collection and analysis, a wheat phenotyping identification robot chassis was developed and a phenotypic detection method for key wheat growth stages was proposed based on a phenotyping robot. Initially, a TD-YOLO v11 seedling detection model was proposed to achieve automated and precise recognition of wheat seedling emergence in the field. The incorporation of the DCNv4 module into the feature extraction network enhanced its ability to capture contextual information, allowing for the extraction of feature representations with fewer network parameters, thereby reducing computational complexity and the number of parameters. Moreover, the introduction of a task dynamic alignment detection head further utilized information from intermediate layers, promoting consistency between classification and localization tasks, and improving the model’s classification and localization performance during seedling detection. Subsequently, a phenotyping identification system for wheat was constructed, integrating multi-sensor fusion and edge computing. This system integrated the seedling detection method with previously proposed phenotyping identification techniques for heading stage monitoring and flowering stage determination, enabling the efficient and automated collection and analysis of field phenotypic data. The results indicated that the proposed method had a high accuracy in wheat seedling emergence identification (R2=0.908, RMSE=11.73, rRMSE=23.04%). It also enabled dynamic monitoring of wheat heading and flowering stages, exhibiting excellent temporal feature capture capabilities. The system facilitated precise determination of wheat growth stages and accurate analysis of key phenotypic traits, including spike number, spikelet number, flower number, and seedling emergence. This method can be applied for high throughput collection and efficient analysis of field wheat phenotypic data, providing effective and reliable technical support for field phenotype acquisition in wheat breeding.