{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:05:45Z","timestamp":1780549545805,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,7]],"date-time":"2020-01-07T00:00:00Z","timestamp":1578355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Egg Industry Center (EIC)","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"USDA National Programs","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Mississippi Agricultural and Forestry Experiment Station","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"USDA National Institute of Food and Agriculture","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The manual collection of eggs laid on the floor (or \u2018floor eggs\u2019) in cage-free (CF) laying hen housing is strenuous and time-consuming. Using robots for automatic floor egg collection offers a novel solution to reduce labor yet relies on robust egg detection systems. This study sought to develop vision-based floor-egg detectors using three Convolutional Neural Networks (CNNs), i.e., single shot detector (SSD), faster region-based CNN (faster R-CNN), and region-based fully convolutional network (R-FCN), and evaluate their performance on floor egg detection under simulated CF environments. The results show that the SSD detector had the highest precision (99.9 \u00b1 0.1%) and fastest processing speed (125.1 \u00b1 2.7 ms\u00b7image\u22121) but the lowest recall (72.1 \u00b1 7.2%) and accuracy (72.0 \u00b1 7.2%) among the three floor-egg detectors. The R-FCN detector had the slowest processing speed (243.2 \u00b1 1.0 ms\u00b7image\u22121) and the lowest precision (93.3 \u00b1 2.4%). The faster R-CNN detector had the best performance in floor egg detection with the highest recall (98.4 \u00b1 0.4%) and accuracy (98.1 \u00b1 0.3%), and a medium prevision (99.7 \u00b1 0.2%) and image processing speed (201.5 \u00b1 2.3 ms\u00b7image\u22121); thus, the faster R-CNN detector was selected as the optimal model. The faster R-CNN detector performed almost perfectly for floor egg detection under a wide range of simulated CF environments and system settings, except for brown egg detection at 1 lux light intensity. When tested under random settings, the faster R-CNN detector had 91.9\u201394.7% precision, 99.8\u2013100.0% recall, and 91.9\u201394.5% accuracy for floor egg detection. It is concluded that a properly-trained CNN floor-egg detector may accurately detect floor eggs under CF housing environments and has the potential to serve as a crucial vision-based component for robotic floor egg collection systems.<\/jats:p>","DOI":"10.3390\/s20020332","type":"journal-article","created":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T03:59:57Z","timestamp":1578455997000},"page":"332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-8051","authenticated-orcid":false,"given":"Guoming","family":"Li","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7809-6514","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbo","family":"Huang","sequence":"additional","affiliation":[{"name":"Agricultural Research Service, Crop Production Systems Research Unit, United States Department of Agriculture, Stoneville, MS 38776, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1017\/S0043933917000812","article-title":"The welfare of layer hens in cage and cage-free housing systems","volume":"73","author":"Hartcher","year":"2017","journal-title":"World\u2019s Poult. Sci. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.applanim.2011.08.016","article-title":"Influence of nest site on the behaviour of laying hens","volume":"135","author":"Lentfer","year":"2011","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.3382\/ps\/pey525","article-title":"Effects of litter floor access and inclusion of experienced hens in aviary housing on floor eggs, litter condition, air quality, and hen welfare","volume":"98","author":"Oliveira","year":"2018","journal-title":"Poult. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.biosystemseng.2018.07.015","article-title":"Evaluation of the performance of PoultryBot, an autonomous mobile robotic platform for poultry houses","volume":"174","author":"Vroegindeweij","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.3382\/ps.2012-02799","article-title":"Housing system and laying hen strain impacts on egg microbiology","volume":"92","author":"Jones","year":"2013","journal-title":"Poult. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/japr\/7.3.225","article-title":"Performance and egg quality of laying hens in an aviary system","volume":"7","author":"Abrahamsson","year":"1998","journal-title":"J. Appl. Poult. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compag.2013.05.004","article-title":"Robust pixel-based classification of obstacles for robotic harvesting of sweet-pepper","volume":"96","author":"Bac","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1002\/rob.21709","article-title":"Performance evaluation of a harvesting robot for sweet pepper","volume":"34","author":"Bac","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_9","unstructured":"Hiremath, S., van Evert, F., Heijden, V., ter Braak, C., and Stein, A. (2012, January 7\u201312). Image-based particle filtering for robot navigation in a maize field. Proceedings of the Workshop on Agricultural Robotics (IROS 2012), Vilamoura, Portugal."},{"key":"ref_10","unstructured":"Vroegindeweij, B.A., Kortlever, J.W., Wais, E., and van Henten, E.J. (2014, January 6\u201310). Development and test of an egg collecting device for floor eggs in loose housing systems for laying hens. Presented at the International Conference of Agricultural Engineering AgEng 2014, Zurich, Switzerland."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama, S. (2017, January 21\u201326). Speed\/accuracy trade-offs for modern convolutional object detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). SSD: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-FCN: Object detection via region-based fully convolutional networks. Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain."},{"key":"ref_14","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). In Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.compag.2018.09.030","article-title":"Dairy goat detection based on Faster R-CNN from surveillance video","volume":"154","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.compag.2018.11.002","article-title":"Feeding behavior recognition for group-housed pigs with the Faster R-CNN","volume":"155","author":"Yang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nasirahmadi, A., Sturm, B., Edwards, S., Jeppsson, K.-H., Olsson, A.-C., M\u00fcller, S., and Hensel, O. (2019). Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs. Sensors, 19.","DOI":"10.3390\/s19173738"},{"key":"ref_18","unstructured":"Huang, J., Rathod, V., Chow, D., Sun, C., Zhu, M., Fathi, A., and Lu, Z. (2019, May 05). Tensorflow Object Detection API. Available online: https:\/\/github.com\/tensorflow\/models\/tree\/master\/research\/object_detection."},{"key":"ref_19","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","unstructured":"(2019, July 22). Google Cloud Creating an Object Detection Application Using TensorFlow. Available online: https:\/\/cloud.google.com\/solutions\/creating-object-detection-application-tensorflow."},{"key":"ref_23","unstructured":"Japkowicz, N. (2006, January 16\u201317). Why question machine learning evaluation methods. Proceedings of the AAAI Workshop on Evaluation Methods for Machine Learning, Boston, MA, USA."},{"key":"ref_24","first-page":"2935","article-title":"A survey of accuracy evaluation metrics of recommendation tasks","volume":"10","author":"Gunawardana","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, L.-C., Lai, K.R., and Zhang, X. (2016). Dimensional sentiment analysis using a regional CNN-LSTM model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics.","DOI":"10.18653\/v1\/P16-2037"},{"key":"ref_26","unstructured":"Zhang, T., Liu, L., Zhao, K., Wiliem, A., Hemson, G., and Lovell, B. (2018). Omni-supervised joint detection and pose estimation for wild animals. Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pacha, A., Choi, K.-Y., Co\u00fcasnon, B., Ricquebourg, Y., Zanibbi, R., and Eidenberger, H. (2018, January 24\u201327). Handwritten music object detection: Open issues and baseline results. Proceedings of the 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), Vienna, Austria.","DOI":"10.1109\/DAS.2018.51"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017, January 18\u201321). Residual and plain convolutional neural networks for 3D brain MRI classification. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"ref_29","unstructured":"Adam, C. (2019, August 13). Egg Lab Results. Available online: https:\/\/adamcap.com\/schoolwork\/1407\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Okafor, E., Berendsen, G., Schomaker, L., and Wiering, M. (2018). Detection and Recognition of Badgers Using Deep Learning. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-01424-7_54"},{"key":"ref_31","unstructured":"Vanhoucke, V., Senior, A., and Mao, M.Z. (2011, January 10). Improving the speed of neural networks on CPUs. Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS 2010), Whistler, BC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"13778","DOI":"10.3390\/s140813778","article-title":"Automated detection and recognition of wildlife using thermal cameras","volume":"14","author":"Christiansen","year":"2014","journal-title":"Sensors"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:42:23Z","timestamp":1760362943000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,7]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020332"],"URL":"https:\/\/doi.org\/10.3390\/s20020332","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,7]]}}}