外文摘要：In the aquaculture phase, ensuring the safe transportation of sturgeon is crucial. The stress levels experienced during transit directly impact the fish quality and the economic returns for farmers. To address this, distributors enlist fishery farming experts to evaluate sturgeon stress. Our investigation identified three critical parameters for grading: sturgeon physiological, environmental, and visual characteristics. This study aims to develop a Cross-Modal Stress Classification Network (CM-SCN) model. It integrates information from three sensing systems to assess sturgeon stress levels. The model is built upon the architectures of AlexNet and ANN, skillfully combining both image and non-image data sources. This integration enables the model to effectively categorize sturgeon stress into four classes: A, B, C, D (representing mild, minor, moderate, and severe stress levels). The results demonstrated the model's high performance with an accuracy of 88.96%, precision of 90.06%, recall of 89.43%, and an F1 score of 89.49%. Notably, the CM-SCN model surpassed both the unimodal visual stress model and the bimodal physiological-environmental stress model. This study introduces an efficient and dependable method for monitoring sturgeon health, offering promising advancements in the field.
外文关键词：deep learning;Data Fusion;Multi-sensing technology;Non-destructive classification;Multiple input
作者：Xia, Jie;Zhang, Xiaoshuan;Huang, Wentao;Glamuzina, Branko;Wang, Yangfeng;Jin, Xinyi;Zhu, Hongliang;Yu, Wenyong
作者单位：Zhejiang Univ;China Agr Univ;Huazhong Univ Sci & Technol;Univ Dubrovnik
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：220
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台