{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:26:38Z","timestamp":1768519598165,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2001000229"],"award-info":[{"award-number":["2001000229"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.<\/jats:p>","DOI":"10.3390\/s20174992","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T08:40:26Z","timestamp":1599122426000},"page":"4992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8732-1111","authenticated-orcid":false,"given":"Shuli","family":"Xing","sequence":"first","affiliation":[{"name":"Center for Advanced Image and Information Technology, School of Electronics &amp; Information Engineering, Chon Buk National University, Jeonju, Chon Buk 54896, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malrey","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Advanced Image and Information Technology, School of Electronics &amp; Information Engineering, Chon Buk National University, Jeonju, Chon Buk 54896, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al Bashish, D., Braik, M., and Bani-Ahmad, S. (2010, January 15\u201317). A framework for detection and classification of plant leaf and stem diseases. Proceedings of the 2010 International Conference on Signal and Image Processing, Chennai, India.","DOI":"10.1109\/ICSIP.2010.5697452"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2017.04.008","article-title":"Symptom based automated detection of citrus diseases using color histogram and textural descriptors","volume":"138","author":"Ali","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.biosystemseng.2009.07.002","article-title":"Local feature-based identification and classification for orchard insects","volume":"104","author":"Wen","year":"2009","journal-title":"Biosyst. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.compag.2015.10.015","article-title":"Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning","volume":"119","author":"Xie","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","article-title":"A comparative study of fine-tuning deep learning models for plant disease identification","volume":"161","author":"Too","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","article-title":"Pest identification via deep residual learning in complex background","volume":"141","author":"Cheng","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.compag.2017.11.039","article-title":"Detection of stored-grain insects using deep learning","volume":"145","author":"Shen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alom, M., Tha, T., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M., Hasan, M., Essen, B., Awwal, A., and Asari, V. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_11","unstructured":"He, K., Girshick, R., and Doll\u00e1r, P. (November, January 27). Rethinking imagenet pre-training. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_13","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway Networks. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_16","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS), Montreal, PQ, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H., and Sun, J. (2018). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. arXiv.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xing, S., Lee, M., and Lee, K.K. (2019). Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors, 19.","DOI":"10.3390\/s19143195"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105488","DOI":"10.1016\/j.compag.2020.105488","article-title":"Cotton pests classification in field-based images using deep residual networks","volume":"174","author":"Alves","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105393","DOI":"10.1016\/j.compag.2020.105393","article-title":"Using deep transfer learning for image-based plant disease identification","volume":"173","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_26","unstructured":"Larsson, G., Maire, M., and Shakhnarovich, G. (2017, January 24\u201326). Fractalnet: Ultra-deep neural networks without residuals. Proceedings of the 5th International Conference on Learning Representations, Toulon, France."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2017). Squeeze-and-Excitation Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_30","unstructured":"Recht, B., Roelofs, R., Schmidt, L., and Shankar, V. (2018). Do cifar-10 classifiers generalize to cifar-10?. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2917536","DOI":"10.1155\/2017\/2917536","article-title":"Automatic image-based plant disease severity estimation using deep learning","volume":"2017","author":"Wang","year":"2017","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.biosystemseng.2019.04.007","article-title":"Influence of image quality on the identification of psyllids using convolutional neural networks","volume":"182","author":"Barbedo","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104906","DOI":"10.1016\/j.compag.2019.104906","article-title":"Crop pest classification based on deep convolutional neural network and transfer learning","volume":"164","author":"Thenmozhi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_35","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_36","unstructured":"Engstrom, L., Tsipras, D., Schmidt, L., and Madry, A. (2017). A rotation and a translation suffice: Fooling cnns with simple transformations. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_38","unstructured":"Ng, A.Y. (2004, January 4\u20138). Feature selection, L1 vs. L2 regularization, and rotational invariance. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada."},{"key":"ref_39","unstructured":"Sutskever, I., Martens, J., Dahl, G.E., and Hinton, G.E. (2013, January 16\u201321). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning (ICML), Atlanta, GA, USA."},{"key":"ref_40","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4992\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:13Z","timestamp":1760177173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,3]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174992"],"URL":"https:\/\/doi.org\/10.3390\/s20174992","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,3]]}}}