{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:54:17Z","timestamp":1775192057011,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Pilot Projects of the Agri-Tech Extension and Service in Shaanxi","award":["2016XXPT-00"],"award-info":[{"award-number":["2016XXPT-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The fertility detection of Specific Pathogen Free (SPF) chicken embryo eggs in vaccine preparation is a challenging task due to the high similarity among six kinds of hatching embryos (weak, hemolytic, crack, infected, infertile, and fertile). This paper firstly analyzes two classification difficulties of feature similarity with subtle variations on six kinds of five- to seven-day embryos, and proposes a novel multi-feature fusion based on Deep Convolutional Neural Network (DCNN) architecture in a small dataset. To avoid overfitting, data augmentation is employed to generate enough training images after the Region of Interest (ROI) of original images are cropped. Then, all the augmented ROI images are fed into pretrained AlexNet and GoogLeNet to learn the discriminative deep features by transfer learning, respectively. After the local features of Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG) are extracted, the multi-feature fusion with deep features and local features is implemented. Finally, the Support Vector Machine (SVM) is trained with the fused features. The verified experiments show that this proposed method achieves an average classification accuracy rate of 98.4%, and that the proposed transfer learning has superior generalization and better classification performance for small-scale agricultural image samples.<\/jats:p>","DOI":"10.3390\/sym11050606","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T03:15:22Z","timestamp":1556766922000},"page":"606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3333-5718","authenticated-orcid":false,"given":"Lvwen","family":"Huang","sequence":"first","affiliation":[{"name":"College of Information Engineering, NorthWest A&amp;F University, Yangling 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China"}]},{"given":"Along","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Engineering, NorthWest A&amp;F University, Yangling 712100, China"}]},{"given":"Mengqun","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, NorthWest A&amp;F University, Yangling 712100, China"}]},{"given":"Yuxi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, NorthWest A&amp;F University, Yangling 712100, China"}]},{"given":"Ruige","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Information Engineering, NorthWest A&amp;F University, Yangling 712100, China"}]},{"given":"Xiaolin","family":"Nie","sequence":"additional","affiliation":[{"name":"College of Information Engineering, NorthWest A&amp;F University, Yangling 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, Q., and Cui, F. 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