{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:13:56Z","timestamp":1762272836123,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.<\/jats:p>","DOI":"10.3390\/s21237929","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6417-4646","authenticated-orcid":false,"given":"Jianqiang","family":"Lu","sequence":"first","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"},{"name":"National International Joint Research Center of Precision Agriculture Aviation Application Technology, Guangzhou 510642, China"},{"name":"Lingnan Modern Agriculture Guangdong Laboratory, Guangzhou 510642, China"}]},{"given":"Weize","family":"Lin","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Pingfu","family":"Chen","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"},{"name":"National International Joint Research Center of Precision Agriculture Aviation Application Technology, Guangzhou 510642, China"},{"name":"Lingnan Modern Agriculture Guangdong Laboratory, Guangzhou 510642, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3443","authenticated-orcid":false,"given":"Xiaoling","family":"Deng","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"},{"name":"National International Joint Research Center of Precision Agriculture Aviation Application Technology, Guangzhou 510642, China"},{"name":"Lingnan Modern Agriculture Guangdong Laboratory, Guangzhou 510642, China"}]},{"given":"Hongyu","family":"Niu","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Jiawei","family":"Mo","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Jiaxing","family":"Li","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Shengfu","family":"Luo","sequence":"additional","affiliation":[{"name":"School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"215","article-title":"Pedestrian detection method based on yolo network","volume":"44","author":"Gao","year":"2018","journal-title":"Comput. Eng."},{"key":"ref_2","first-page":"41","article-title":"Real-time vehicle detection based on yolo algorithm","volume":"38","author":"Wang","year":"2016","journal-title":"J. Wuhan Univ. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, R., Zhou, J., Zheng, Y., Ke, Q., Zhi, Y., Guan, H., Wu, X., and Zhai, Y. (2019). Communication Base-Station Antenna Detection Algorithm Based on YOLOv3-Darknet Network. International Conference on Intelligent and Interactive Systems and Applications, Springer.","DOI":"10.1007\/978-3-030-34387-3_81"},{"key":"ref_4","first-page":"172","article-title":"Robot picking apple positioning based on yolo deep convolu-tional neural network under complex background","volume":"35","author":"Zhao","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_5","first-page":"174","article-title":"Flower recognition system based on residual network migration learning","volume":"55","author":"Guan","year":"2019","journal-title":"J. Comput. Eng. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s11119-021-09808-9","article-title":"Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer","volume":"22","author":"Khan","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_7","first-page":"90","article-title":"Recognition of Flower Varieties Based on Convolutional Neural Network","volume":"10","author":"Yang","year":"2019","journal-title":"Eng. J. Heilongjiang Univ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-021-00708-7","article-title":"Correction to: Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model","volume":"17","author":"Liu","year":"2021","journal-title":"Plant Methods"},{"key":"ref_9","unstructured":"Wang, M., Chen, R., Ran, L.Z., Yang, X., Zhang, X.R., Yao, J.T., Luo, Y.S., and Ai, M. (2021). Identification Method of Citrus Red Spider Pests Based on Deep Learning. (CN112597907A)."},{"key":"ref_10","unstructured":"Wang, X. (2020). Research on Image Segmentation of Multi-Variety Fruits and Flowers Based on Deep Learning. [Master\u2019s Thesis, Wuhan University of Light Industry]."},{"key":"ref_11","first-page":"200","article-title":"Citrus flower identification and flower volume statistics based on instance segmentation","volume":"36","author":"Deng","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, K., and Liu, M. (2021). YOLOv3-MT: A YOLOv3 using multi-target tracking for vehicle visual detection. Appl. Intell., 1\u201322.","DOI":"10.1007\/s10489-021-02491-3"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, IEEE. [1st ed.].","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2016). YOLO9000: Better, faster, stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_18","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_19","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-J.M. (2020). YOLOv4 Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, J., Li, J., Niu, S., Wu, L., and Song, H. (2021). Zero-bias Deep Learning Enabled Quickest Abnormal Event Detection in IoT. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2021.3126819"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.procir.2019.04.107","article-title":"Product-oriented Product Service System for Large-scale Vision In-spection","volume":"83","author":"Zhou","year":"2019","journal-title":"Procedia CIRP"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2016). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1111\/1365-2435.12973","article-title":"Variation of stomatal traits from cold tem-perate to tropical forests and association with water use efficiency","volume":"32","author":"Liu","year":"2018","journal-title":"Funct. Ecol."},{"key":"ref_24","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shen, S., Dong, Z., Ye, J., Ma, L., Yao, Z., Gholami, A., Mahoney, M.W., and Keutzer, K. (2020, January 3). Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto, CA, USA.","DOI":"10.1609\/aaai.v34i05.6409"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zafrir, O., Boudoukh, G., Izsak, P., and Wasserblat, M. (2019). Q8bert: Quantized 8bit bert. arXiv.","DOI":"10.1109\/EMC2-NIPS53020.2019.00016"},{"key":"ref_27","unstructured":"Han, S.M.H., and Dally, W.J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture de-sign. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_31","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_32","unstructured":"Tan, M., and Le, Q.V. (2021). Efficientnetv2: Smaller models and faster training. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). GhostNet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 27\u201328). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_35","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., and Sun, J. (2019, January 27\u201328). ThunderNet: Towards real-time generic object detection on mobile devices. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00682"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain Adaptation via Transfer Component Analysis","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1007\/s10489-020-01818-w","article-title":"Light-YOLOv3: Fast method for detecting green mangoes in complex scenes using picking robots","volume":"50","author":"Xu","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_40","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA."},{"key":"ref_41","unstructured":"Devries, T., and Taylor, G.W. (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, Y., Wang, J., Niu, S., Liu, D., and Song, H. (2021). Zero-bias Deep Neural Network for Quickest RF Signal Surveillance. arXiv.","DOI":"10.1109\/IPCCC51483.2021.9679426"},{"key":"ref_43","unstructured":"Liu, Y., Wang, J., Li, J., Niu, S., and Song, H. (2021). Machine learning for the detection and identification of internet of things (iot) devices: A survey. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1007\/s11119-020-09709-3","article-title":"Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images","volume":"21","author":"Tu","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3464324","article-title":"Cross-Modality Transfer Learning for Image-Text Information Management","volume":"13","author":"Niu","year":"2022","journal-title":"ACM Trans. Manag. Inf. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A decade survey of transfer learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Trans. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7929\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:50Z","timestamp":1760168210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7929"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":46,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21237929"],"URL":"https:\/\/doi.org\/10.3390\/s21237929","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,11,27]]}}}