Traditional machine learning in plant phenotyping research requires the assistance of professional data scientists and domain experts to adjust the structure and hy-perparameters tuning of neural network models with much human intervention, making the model training and deployment ineffective. In this paper, the automated machine learning method is researched to construct a multi-task learning model for Arabidopsis thaliana genotype classification, leaf number, and leaf area regression tasks. The experimental results show that the genotype classification task’s accuracy and recall achieved 98.78%, precision reached 98.83%, and classification F₁ value reached 98.79%, as well as the R² of leaf number regression task and leaf area regression task reached 0.9925 and 0.9997 respectively. The experimental results demonstrated that the multi-task automated machine learning model can combine the benefits of multi-task learning and automated machine learning, which achieved more bias information from related tasks and improved the overall classification and prediction effect. Additionally, the model can be created automatically and has a high degree of generalization for better phenotype reasoning. In addition, the trained model and system can be deployed on cloud platforms for convenient application.

Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and $F_w$ scores of $96.88\%$, $97.50\%$, and $96.74\%$, respectively. Furthermore, the coefficient of determination $R^2$ values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.