{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:07:14Z","timestamp":1771466834884,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971797"],"award-info":[{"award-number":["31971797"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601189"],"award-info":[{"award-number":["61601189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General program of Guangdong Natural Science Foundation","award":["2021A1515010923"],"award-info":[{"award-number":["2021A1515010923"]}]},{"name":"China Agriculture Research System of MOF and MARA","award":["CARS-26"],"award-info":[{"award-number":["CARS-26"]}]},{"name":"Special projects for key fields of colleges and universities in Guangdong Province","award":["2020ZDZX3061"],"award-info":[{"award-number":["2020ZDZX3061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Florescence information monitoring is essential for strengthening orchard management activities, such as flower thinning, fruit protection, and pest control. A lightweight object recognition model using cascade fusion YOLOv4-CF is proposed, which recognizes multi-type objects in their natural environments, such as citrus buds, citrus flowers, and gray mold. The proposed model has an excellent representation capability with an improved cascade fusion network and a multi-scale feature fusion block. Moreover, separable deep convolution blocks were employed to enhance object feature information and reduce model computation. Further, channel shuffling was used to address missing recognition in the dense distribution of object groups. Finally, an embedded sensing system for recognizing citrus flowers was designed by quantitatively applying the proposed YOLOv4-CF model to an FPGA platform. The mAP@.5 of citrus buds, citrus flowers, and gray mold obtained on the server using the proposed YOLOv4-CF model was 95.03%, and the model size of YOLOv4-CF + FPGA was 5.96 MB, which was 74.57% less than the YOLOv4-CF model. The FPGA side had a frame rate of 30 FPS; thus, the embedded sensing system could meet the demands of florescence information in real-time monitoring.<\/jats:p>","DOI":"10.3390\/s22031255","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:36:42Z","timestamp":1644266202000},"page":"1255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Embedded Sensing System for Recognizing Citrus Flowers Using Cascaded Fusion YOLOv4-CF + FPGA"],"prefix":"10.3390","volume":"22","author":[{"given":"Shilei","family":"Lyu","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"},{"name":"Division of Citrus Machinery, China Agriculture Research System of MOF and MARA, Guangzhou 510642, China"}]},{"given":"Yawen","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Ruiyao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"},{"name":"Division of Citrus Machinery, China Agriculture Research System of MOF and MARA, Guangzhou 510642, China"}]},{"given":"Renjie","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Qiafeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/1744-7917.12000","article-title":"Mechanisms for flowering plants to benefit arthropod natural enemies of insect pests: Prospects for enhanced use in agriculture","volume":"21","author":"Lu","year":"2014","journal-title":"Insect Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Herz, A., Cahenzli, F., Penvern, S., Pfiffner, L., Tasin, M., and Sigsgaard, L. (2019). Managing floral resources in apple orchards for pest control: Ideas, experiences and future directions. Insects, 10.","DOI":"10.3390\/insects10080247"},{"key":"ref_3","first-page":"143","article-title":"Tomato florescence recognition and detection method based on cascaded neural network","volume":"36","author":"Zhao","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105742","DOI":"10.1016\/j.compag.2020.105742","article-title":"Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments","volume":"178","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1354007","DOI":"10.1142\/S0218001413540074","article-title":"A novel technique for tangerine yield prediction using flower detection algorithm","volume":"27","author":"Dorj","year":"2013","journal-title":"Int. J. Pattern Recogn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1109\/LRA.2018.2849498","article-title":"Multispecies fruit flower detection using a refined semantic segmentation network","volume":"3","author":"Dias","year":"2018","journal-title":"IEEE Robot. Autom. Let."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106150","DOI":"10.1016\/j.compag.2021.106150","article-title":"Apple, peach, and pear flower detection using semantic segmentation network and shape constraint level set","volume":"185","author":"Sun","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","first-page":"22","article-title":"A Recognition Method of Kiwifruit Flowers Based on K-means Clustering","volume":"42","author":"Liu","year":"2020","journal-title":"J. Agric. Mech. Res."},{"key":"ref_9","first-page":"109","article-title":"Research on strawberry flower recognition algorithm based on image processing","volume":"37","author":"Cui","year":"2019","journal-title":"Dig. Technol. Appl."},{"key":"ref_10","first-page":"1","article-title":"Application research of Mask R-CNN model in the identification of eggplant flower blooming period","volume":"15","author":"Zheng","year":"2021","journal-title":"Comput. Eng. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11119-019-09673-7","article-title":"A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field","volume":"21","author":"Lin","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_12","first-page":"252","article-title":"Litchi flower and leaf segmentation and recognition based on deep semantic segmentation","volume":"52","author":"Xiong","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_13","first-page":"200","article-title":"Recognition and counting of citrus flowers based on instance segmentation","volume":"36","author":"Deng","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.compag.2018.06.040","article-title":"Machine vision assessment of mango orchard flowering","volume":"151","author":"Wang","year":"2018","journal-title":"Comput. Electr. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhou, J., Xu, C.-Y., and Bai, X. (2020, January 19\u201323). In a deep object detection method for pineapple fruit and flower recognition in cluttered background. Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, Zhongshan, China.","DOI":"10.1007\/978-3-030-59830-3_19"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105796","DOI":"10.1016\/j.compag.2020.105796","article-title":"Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions","volume":"178","author":"Palacios","year":"2020","journal-title":"Comput. Electr. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1364-3703.2007.00417.x","article-title":"Botrytis cinerea: The cause of grey mould disease","volume":"8","author":"Williamson","year":"2007","journal-title":"Mol. Plant Pathol."},{"key":"ref_18","first-page":"1074","article-title":"Investigation of surface defect of Citrus fruits caused by Botrytis-Molded petals","volume":"29","author":"Zhu","year":"2012","journal-title":"J. Fruit Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Que, L., Zhang, T., Guo, H., Jia, C., Gong, Y., Chang, L., and Zhou, J. (2021). A lightweight pedestrian detection engine with two-stage low-complexity detection network and adaptive region focusing technique. Sensors, 21.","DOI":"10.3390\/s21175851"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, I., and Figueroa, M. (2021). A heterogeneous hardware accelerator for image classification in embedded systems. Sensors, 21.","DOI":"10.3390\/s21082637"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Luo, Y., and Chen, Y. (2021). FPGA-Based Acceleration on Additive Manufacturing Defects Inspection. Sensors, 21.","DOI":"10.3390\/s21062123"},{"key":"ref_22","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_23","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_25","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 design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201321). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, Detection and Segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83224","DOI":"10.1109\/ACCESS.2020.2988311","article-title":"An efficient task assignment framework to accelerate DPU-based convolutional neural network inference on FPGAs","volume":"8","author":"Zhu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vandendriessche, J., Wouters, N., da Silva, B., Lamrini, M., Chkouri, M.Y., and Touhafi, A. (2021). Environmental sound recognition on embedded systems: From FPGAs to TPUs. Electronics, 10.","DOI":"10.3390\/electronics10212622"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_32","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, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_33","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.N., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:25Z","timestamp":1760134525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031255"],"URL":"https:\/\/doi.org\/10.3390\/s22031255","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,7]]}}}