Recent achievements of the team - A multi-task learning framework for integrated assessment in agricultural applications
Our team published the paper “A multi-task learning framework for integrated assessment in agricultural applications” in Information Sciences (IF: 6.8, QSCI Zone 2). The School of Computer Engineering and Science at Shanghai University is the first institution listed.
Automated assessment of fruits and vegetables is an important task in smart agriculture, quality control, and supply chain management. Traditional manual weighing and visual inspection are time-consuming, labor-intensive, and highly subjective, while most existing automated methods focus on a single task and struggle to perform comprehensive multi-attribute assessment within a unified framework. In addition, datasets with multi-attribute annotations for fruits and vegetables remain limited. To address this problem, this paper proposes a multi-task deep learning framework for agricultural applications, capable of simultaneously performing weight prediction, key phenotypic feature analysis, and quality grade classification from a single RGB image.
Specifically, this paper constructs FruVegSet (FVS), an integrated assessment dataset for fruits and vegetables, covering two types of agricultural products, cucumbers and bananas, and providing multi-attribute annotations including images, weight, key phenotypic features, and quality grades. In terms of model design, this paper adopts a ResNet18-based pre-classification module to identify the category of agricultural products and route input images to corresponding category-specific subnetworks. Then, task-related features are extracted through the weight branch and key phenotype branch, respectively. A feature pyramid network is introduced to enhance morphological feature representation, while a large-kernel attention fusion module and cross-attention mechanism are combined to enable information interaction between tasks. Finally, the model simultaneously predicts weight, analyzes key phenotypic features, and classifies quality grades to complete integrated assessment. Experimental results show that the proposed framework achieves favorable integrated assessment performance on both cucumber and banana data, outperforming single-task models and representative agricultural quality classification models.
Essay: A multi-task learning framework for integrated assessment in agricultural applications
