Congratulations to Ge Jiaohao on his successful graduation!

Ge Jiaohao graduated from Shanghai University and began pursuing an academic master’s degree in the School of Computer Engineering and Science at Shanghai University in 2023. After joining the research group, he studied small-sample image processing and multi-task learning techniques and their applications in agriculture under the guidance of Professor Han Yuexing. With the careful guidance of Professor Han, he completed the following research:

1. To address the problem of multi-task feature interference and the difficulty of coordinating regression classification under small-sample conditions, a multi-task learning-based joint evaluation framework for multiple vegetable and fruit attributes is proposed. This method includes pre-classification routing, task branch modeling, feature enhancement and cross-branch interaction, and joint loss optimization. To support the training and evaluation of this method, the FruVegSet multi-attribute alignment dataset was further constructed, achieving one-to-one correspondence between images, continuous attributes, and grade labels based on weight, curvature, and maturity measurement results combined with grade mapping rules. Under the current data scale and experimental protocol, compared to single-task models and other multi-task models, this method achieves better comprehensive performance on cucumber and banana data, particularly showing stable performance in task balancing and grade discrimination.

2. To address the issue of missing transition states between endpoint samples under small-sample conditions for continuous attributes, a multi-task extension learning method based on intermediate sample generation is further proposed on the basis of the multi-task joint evaluation framework. This method generates intermediate transition samples through DiffMorpher, and combines the SAM2 segmentation model, subject mask screening strategy, and task-specific pseudo-label construction to improve the quality of supplementary supervision. Under the effect of this strategy, the method achieves stable improvements in critical phenotype feature regression and grade classification tasks, and to some extent improves the overall performance of the three tasks.

During his graduate studies at Shanghai University, Ge Jiaohao worked hard to improve his professional level and research capabilities, and made many good friends and mentors. We hope that Ge Jiaohao will pursue his future path with ideals in his heart, fear no hardship, and move forward bravely.

Essay: Research on Small Sample Multi-task Learning Methods for Multi-Attribute Joint Evaluation of Agricultural Fruits and Vegetables

Code: https://github.com/han-yuexing/2026-thesis-gjh-code