Visual-Based Flower Counting: Techniques and Applications

Siyang Zang; Lei Shu; Ru Han; Xing Yang; Grzegorz Cielniak; Feiya Zhang

doi:10.1109/JAS.2026.126050

Volume 13 Issue 7

Jul. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(7): 1536-1557

S. Zang, L. Shu, R. Han, X. Yang, G. Cielniak, and F. Zhang, “Visual-based flower counting: Techniques and applications,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1536–1557, Jul. 2026. doi: 10.1109/JAS.2026.126050

Citation:

S. Zang, L. Shu, R. Han, X. Yang, G. Cielniak, and F. Zhang, “Visual-based flower counting: Techniques and applications,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1536–1557, Jul. 2026. doi: 10.1109/JAS.2026.126050

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Visual-Based Flower Counting: Techniques and Applications

doi: 10.1109/JAS.2026.126050

Funds: This work was supported in part by the Foundation of Key Laboratory of Landscaping (KFL202402), the Ministry of Agriculture and Rural Affairs, China, and the Research Start-Up Fund for Talent Introduction of Nanjing Agricultural University (77H0603)

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Author Bio:
Siyang Zang (Student Member, IEEE) received the M.S. degree from Nanjing Agricultural University in 2024. He is currently pursuing the Ph.D. degree in intelligent science and technology with the College of Artificial Intelligence, Nanjing Agricultural University. His research interests include computer vision, intelligent agricultural perception, plant phenotyping, and UAV remote sensing

Lei Shu (Senior Member, IEEE) received the B.S. degree in computer science from South-Central Minzu University in 2002, the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland, in 2010. Until 2012, he was a Specially Assigned Researcher with the Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Japan. He is currently a Distinguished Professor with Nanjing Agricultural University and a Lincoln Professor with the University of Lincoln, U.K. He is also the Director of the NAULincoln Joint Research Center of Intelligent Engineering. He has published more than 400 articles in related conferences, journals, and books in the areas of sensor networks and Internet of Things. His current H-index is 72, and i10-index is 302 in Google Scholar Citation. His current research interests include wireless sensor networks and the Internet of Things. He has also served as a technical program committee (TPC) member for more than 150 international conferences, including major IEEE conferences in communications, mobile computing, sensor networks, and the Internet of Things. He was a recipient of the 2014 Top-Level Talents in the Sailing Plan of Guangdong Province, China, the 2015 Outstanding Young Professor of Guangdong Province, and several best paper awards and editorial awards. He has also served as a Co-Chair for more than 50 international conferences and workshops in related areas

Ru Han (Student Member, IEEE) received the M.S. degree from Jiangxi Normal University in 2020. She is currently working toward the Ph.D. degree, majoring in intelligent science and technology, with the College of Artificial Intelligence, Nanjing Agricultural University. Her current research interests include computer vision, object detection, smart agriculture, and plant disease detection

Xing Yang (Member, IEEE) received the Ph.D. degree in agricultural electrification and automation with the College of Engineering, Nanjing Agricultural University in 2023. Until 2022, he was a visiting Ph.D. student with the University of Lincoln, Lincoln, U.K. He is currently a Lecturer with Anhui Science and Technology University. He has been serving as an Associate Editor for Frontiers in Plant Science-Sustainable and Intelligent Phytoprotection Section. He has authored or co-authored papers in several prestigious journals/conferences, such as IEEE Internet of Things Journal, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Industrial Informatics, and IEEE International Conference on Computer Communications. His current research interests include abnormal detection, edge intelligence, and agricultural IoT

Grzegorz Cielniak received the M.Sc. degree in electrical engineering (automation and robotics) from Wroclaw University of Technology, Poland, in 2000, and the Ph.D. degree in computer science from Orebro University, Sweden, in 2007. He is currently a Professor of robotics with the Lincoln Institute for Agri-food Technology, University of Lincoln, Lincoln, U.K. He previously served as an Associate Professor with the Lincoln Institute for Agri-food Technology and the School of Computer Science, University of Lincoln, U.K., and as an Associate Professor with the Norwegian University of Life Sciences, Norway. He has authored or coauthored more than 100 peer-reviewed publications and has served as a programme committee member and reviewer for a number of international conferences, journals, and funding bodies. His current research interests include robotics and artificial intelligence, particularly robotic perception and navigation in unstructured outdoor environments, with a strong focus on agricultural robotics

Feiya Zhang received the B.S. degree in biomedical engineering from Xinxiang Medical University in 2023. She is currently pursuing the M.S. degree in electronic information with the College of Artificial Intelligence, Nanjing Agricultural University. Her current research interests include computer vision, intelligent agricultural perception, super-resolution reconstruction, and UAV remote sensing
Corresponding author: Lei Shu, e-mail: lei.shu@njau.edu.cn
Received Date: 2025-12-21
Revised Date: 2026-03-10
Accepted Date: 2026-04-07

Available Online: 2026-07-21

Abstract

Abstract

With the rapid integration of modern information technologies into agriculture, smart agriculture enables increasingly precise phenotyping and yield prediction. Flower counting is a key phenological indicator; however, achieving high precision remains a significant challenge due to severe occlusion, density variations, and environmental variability (e.g., lighting/weather). Moreover, existing studies remain fragmented without a comprehensive synthesis. To bridge this fundamental research gap, we present the first systematic survey of computer-vision-based flower counting. In this work, we propose a novel taxonomy that categorizes methods into static (single-image) and dynamic (video/multi-view) paradigms and elucidates their evolutionary trajectory. Unlike conventional reviews, we conduct a multi-scale evaluation encompassing both horizontal (methodological evolution from traditional to deep learning) and vertical (cross-species and scene-condition) performance analyses. Crucially, we validate representative algorithms on deployed platforms—UAV and ground robots—through engineering case studies that quantify real-world trade-offs (e.g., height-accuracy and latency-robustness). Finally, we discuss prevailing limitations and propose future directions, including graph-based reasoning and AgriVerse integration (i.e., agriculture-centric metaverse or digital twin ecosystems), establishing a foundational framework for both academic research and industrial deployment.
- Computer vision,
- deep learning,
- flower counting,
- image processing,
- smart agriculture

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References(143)

References

[1]	D.-L. Yang, Z.-B. Li, H. Wang, and Z.-J. Liu, “Analysis of market demand of the flower industry in major cities,” North. Hortic., no. 13, pp. 134–140, Jul. 2022.
[2]	F. Andrés and G. Coupland, “The genetic basis of flowering responses to seasonal cues,” Nat. Rev. Genet., vol. 13, no. 9, pp. 627–639, Aug. 2012. doi: 10.1038/nrg3291
[3]	Global Industry Analysts, “Flowers and ornamental plants: Global market report 2025,” Global Industry Analysts, Market Research Report Report No. 41264855, San Jose, CA, USA, Jul. 2025.
[4]	J. B. Scott, D. H. Gent, F. S. Hay, and S. J. Pethybridge, “Estimation of pyrethrum flower number using digital imagery,” HortTechnology, vol. 25, no. 5, pp. 617–624, Oct. 2015.
[5]	Y. Wang, M. Kang, Y. Liu, J. Li, K. Xue, X. Wang, J. Du, Y. Tian, Q. Ni, and F.-Y. Wang, “Can digital intelligence and cyber-physical-social systems achieve global food security and sustainability?” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2070–2080, Nov. 2023. doi: 10.1109/JAS.2023.123951
[6]	L. Fu, S. Ling, D. Wu, M. Kang, F.-Y. Wang, and H. Sun, “Parallel seeds: From foundation models to foundation intelligence for agricultural sustainability,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 481–484, Mar. 2025. doi: 10.1109/JAS.2024.124914
[7]	X. Wang, M. Kang, H. Sun, P. de Reffye, and F.-Y. Wang, “DeCASA in AgriVerse: Parallel agriculture for smart villages in Metaverses,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2055–2062, Dec. 2022. doi: 10.1109/JAS.2022.106103
[8]	F. J. Adamsen, T. A. Coffelt, J. M. Nelson, E. M. Barnes, and R. C. Rice, “Method for using images from a color digital camera to estimate flower number,” Crop Sci., vol. 40, no. 3, pp. 704–709, May 2000. doi: 10.2135/cropsci2000.403704x
[9]	U.-O. Dorj, M. Lee, and Diyan-ul-Imaan, “A new method for tangerine tree flower recognition,” in Proc. Int. Conf. Computer Applications for Bio-technology, Multimedia and Ubiquitous City, Gangneug, South Korea, 2012, pp. 49−56.
[10]	N. Bairwa and N. K. Agrawal, “Counting of flowers using image processing,” Int. J. Eng. Res. Technol., vol. 3, no. 9, Art. no. 86740261, Sep. 2014.
[11]	M. P. Diago, A. Sanz-Garcia, B. Millan, J. Blasco, and J. Tardaguila, “Assessment of flower number per inflorescence in grapevine by image analysis under field conditions,” J. Sci. Food Agric., vol. 94, no. 10, pp. 1981–1987, Aug. 2014. doi: 10.1002/jsfa.6512
[12]	P. K. Sethy, B. Routray, and S. K. Behera, “Detection and counting of marigold flower using image processing technique,” in Advances in Computer, Communication and Control: Lecture Notes in Networks and Systems, vol. 41, Singapore: Springer, 2019, pp. 87–93.
[13]	R. Kaur and S. Porwal, “An optimized computer vision approach to precise well-bloomed flower yielding prediction using image segmentation,” Int. J. Comput. Appl., vol. 119, no. 23, pp. 15–20, Jun. 2015.
[14]	B. V. Biradar and S. P. Shrikhande, “Flower detection and counting using morphological and segmentation technique,” Int. J. Comput. Sci. Inf. Technol., vol. 6, no. 3, pp. 2498−2501, 2015.
[15]	V. Sai Sundar and J. Bagyamani, “Flower counting in yield approximation using digital image processing techniques,” Int. J. Adv. Res. Sci. Eng., vol. 4, no. S3, pp. 97–106, Mar. 2015.
[16]	H. Nisar, Z. Y. Hoo, and K. H. Yeap, “Predicting yield of fruit and flowers using digital image analysis,” Indian J. Sci. Technol., vol. 8, no. 32, pp. 1–6, Nov. 2015. doi: 10.17485/ijst/2015/v8i32/93730
[17]	A. Abinaya and S. M. M. Roomi, “Jasmine flower segmentation: A superpixel based approach,” in Proc. Int. Conf. Communication and Electronics Systems, Coimbatore, India, 2016, pp. 1−4.
[18]	W. Zheng, L. Yan, C. Gou, and F.-Y. Wang, “Fuzzy deep forest with deep contours feature for leaf cultivar classification,” IEEE Trans. Fuzzy Syst., vol. 30, no. 12, pp. 5431–5444, Dec. 2022. doi: 10.1109/TFUZZ.2022.3177764
[19]	W. Zheng, C. Gou, and L. Yan, “Forest representation learning with multiscale contour feature learning for leaf cultivar classification,” in Proc. IEEE Int. Conf. Bioinformatics and Biomedicine, San Diego, USA, 2019, pp. 774−777.
[20]	P. R. Dry, M. L. Longbottom, S. McLoughlin, T. E. Johnson, and C. Collins, “Classification of reproductive performance of ten winegrape varieties,” Aust. J. Grape Wine Res., vol. 16, pp. 47–55, Jan. 2010. doi: 10.1111/j.1755-0238.2009.00085.x
[21]	J. Gallmann, B. Schüpbach, K. Jacot, M. Albrecht, J. Winizki, N. Kirchgessner, and H. Aasen, “Flower mapping in grasslands with drones and deep learning,” Front. Plant Sci., vol. 12, Art. no. 774965, Feb. 2022.
[22]	A. Feng, J. Zhou, E. Vories, and K. A. Sudduth, “Evaluation of cotton emergence using UAV-based imagery and deep learning,” Comput. Electron. Agric., vol. 177, Art. no. 105711, Oct. 2020. doi: 10.1016/j.compag.2020.105711
[23]	S. Oh, A. Chang, A. Ashapure, J. Jung, N. Dube, M. Maeda, D. Gonzalez, and J. Landivar, “Plant counting of cotton from UAS imagery using deep learning-based object detection framework,” Remote Sens., vol. 12, no. 18, Art. no. 2981, Sep. 2020. doi: 10.3390/rs12182981
[24]	L. Wang, L. Xiang, L. Tang, and H. Jiang, “A convolutional neural network-based method for corn stand counting in the field,” Sensors, vol. 21, no. 2, Art. no. 507, Jan. 2021. doi: 10.3390/s21020507
[25]	Z. Li, R. Guo, M. Li, Y. Chen, and G. Li, “A review of computer vision technologies for plant phenotyping,” Comput. Electron. Agric., vol. 176, Art. no. 105672, Sep. 2020. doi: 10.1016/j.compag.2020.105672
[26]	A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.
[27]	C. Qi, I. Nyalala, and K. Chen, “Detecting the early flowering stage of tea chrysanthemum using the F-YOLO model,” Agronomy, vol. 11, no. 5, Art. no. 834, Apr. 2021. doi: 10.3390/agronomy11050834
[28]	Y. Jiang and C. Li, “Convolutional neural networks for image-based high-throughput plant phenotyping: A review,” Plant Phenomics, vol. 2020, Art. no. 4152816, Apr. 2020.
[29]	R. Heylen, P. Van Mulders, and N. Gallace, “Counting strawberry flowers on drone imagery with a sequential convolutional neural network,” in Proc. IEEE Int. Geoscience and Remote Sensing Symp., Brussels, Belgium, 2021, pp. 4880−4883.
[30]	Z. Rui, Z.-W. Zhang, M. Zhang, A. Azizi, C. Igathinathane, H. Cen, et al., “High-throughput proximal ground crop phenotyping systems—A comprehensive review,” Comput. Electron. Agric., vol. 224, Art. no. 109108, Sep. 2024.
[31]	S. Nuanmeesri, S. Chopvitayakun, P. Kadmateekarun, and L. Poomhiran, “Marigold flower disease prediction through deep neural network with multimodal image,” Int. J. Eng. Trends Technol., vol. 69, no. 7, pp. 174–180, Jul. 2021.
[32]	B. M. Sagar and N. K. Cauvery, “Agriculture data analytics in crop yield estimation: A critical review,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 3, pp. 1087–1093, Dec. 2018. doi: 10.11591/ijeecs.v12.i3.pp1087-1093
[33]	T. L. Robinson, A. N. Lakso, and D. W. Greene, “Precision crop load management: The practical implementation of physiological models,” in Proc. Int. Symp. Physiological Principles and Their Application to Fruit Production, Geneva, USA, 2017, pp. 381−390.
[34]	T. Behrens, J. Müller, and W. Diepenbrock, “Utilization of canopy reflectance to predict properties of oilseed rape (Brassica napus L.) and barley (Hordeum vulgare L.) during ontogenesis,” Eur. J. Agron., vol. 25, no. 4, pp. 345–355, Nov. 2006. doi: 10.1016/j.eja.2006.06.010
[35]	C. G. Embree, M. T. D. Myra, D. S. Nichols, and A. H. Wright, “Effect of blossom density and crop load on growth, fruit quality, and return bloom in ‘honeycrisp’ apple,” HortScience, vol. 42, no. 7, pp. 1622–1625, Dec. 2007.
[36]	Y. Shang, M. Geng, H. Fang, M. Cai, J. Wang, and H. Song, “Using unmanned aerial vehicle acquired RGB images and density-cluster-count model for tree-level apple flower quantification,” Comput. Electron. Agric., vol. 226, Art. no. 109389, Nov. 2024. doi: 10.1016/j.compag.2024.109389
[37]	H. Link, “Significance of flower and fruit thinning on fruit quality,” Plant Growth Regul., vol. 31, no. 1−2, pp. 17–26, May 2000. doi: 10.1023/A:1006334110068
[38]	A. Ferrara, S. Lovelli, T. Di Tommaso, and M. Perniola, “Flowering, growth and fruit setting in greenhouse bell pepper under water stress,” J. Agron., vol. 10, no. 1, pp. 12–19, Jan. 2011. doi: 10.3923/ja.2011.12.19
[39]	J. Tromp, “Lower-bud formation in pome fruits as affected by fruit thinning,” Plant Growth Regul., vol. 31, no. 1–2, pp. 27–34, May 2000. doi: 10.1023/A:1006342328724
[40]	R. d’Andrimont, M. Taymans, G. Lemoine, A. Ceglar, M. Yordanov, and M. van der Velde, “Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series,” Remote Sens. Environ., vol. 239, Art. no. 111660, Mar. 2020. doi: 10.1016/j.rse.2020.111660
[41]	A. Chaudhury, C. Ward, A. Talasaz, A. G. Ivanov, N. P. Huner, B. Grodzinski, R. V. Patel, and J. L. Barron, “Computer vision based autonomous robotic system for 3D plant growth measurement,” in Proc. 12th Conf. Computer and Robot Vision, Halifax, Canada, 2015, pp. 290−296.
[42]	H. Uchiyama, S. Sakurai, M. Mishima, D. Arita, T. Okayasu, A. Shimada, and R.-I. Taniguchi, “An easy-to-setup 3D phenotyping platform for KOMATSUNA dataset,” in Proc. IEEE Int. Conf. Computer Vision Workshops, Venice, Italy, 2017, pp. 2038−2045.
[43]	T. Park, J. Lee, H. Oh, W.-J. Yun, and K.-W. Lee, “Optimizing indoor farm monitoring efficiency using UAV: Yield estimation in a GNSS-denied cherry tomato greenhouse,” arXiv preprint arXiv: 2505.00995, 2025.
[44]	J. Sánchez-Molina, F. Rodríguez, J. Moreno, J. Sánchez-Hermosilla, and A. Giménez, “Robotics in greenhouses. Scoping review,” Comput. Electron. Agric., vol. 219, Art. no. 108750, Apr. 2024. doi: 10.1016/j.compag.2024.108750
[45]	K. Jindo, O. Kozan, K. Iseki, B. Maestrini, F. K. van Evert, Y. Wubengeda, E. Arai, Y. E. Shimabukuro, Y. Sawada, and C. Kempenaar, “Potential utilization of satellite remote sensing for field-based agricultural studies,” Chem. Biol. Technol. Agric., vol. 8, no. 1, Art. no. 58, Nov. 2021. doi: 10.1186/s40538-021-00253-4
[46]	G. Jang, J. Kim, J.-K. Yu, H.-J. Kim, Y. Kim, D.-W. Kim, K.-H. Kim, C. W. Lee, and Y. S. Chung, “Review: Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application,” Remote Sens., vol. 12, no. 6, Art. no. 998, Mar. 2020. doi: 10.3390/rs12060998
[47]	S. Wu, Y. Zhang, Y. Zhao, W. Wen, C. Wang, X. Lu, M. Guo, X. Guo, J. Zhao, and C. Zhao, “Using high-throughput phenotyping platform MVS-Pheno to decipher the genetic architecture of plant spatial geometric 3D phenotypes for maize,” Comput. Electron. Agric., vol. 225, Art. no. 109259, Oct. 2024. doi: 10.1016/j.compag.2024.109259
[48]	Z. Fan, “Field-based high-throughput phenotyping of maize crops with a robot mobile platform,” Ph.D. dissertation, Northwest AF Univ., Yangling, China, 2022.
[49]	S. Zang, L. Shu, K. Huang, Z. Guan, R. Han, R. Valluru, X. Wang, J. Bao, Y. Zheng, and Y. Chen, “Image dataset of tea chrysanthemums in complex outdoor scenes,” Front. Plant Sci., vol. 14, Art. no. 1134911, Apr. 2023. doi: 10.3389/fpls.2023.1134911
[50]	R. Xu and C. Li, “A review of high-throughput field phenotyping systems: Focusing on ground robots,” Plant Phenomics, vol. 2022, Art. no. 9760269, Jun. 2022. doi: 10.34133/2022/9760269
[51]	Y. Li, W. Wen, T. Miao, S. Wu, Z. Yu, X. Wang, X. Guo, and C. Zhao, “Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning,” Comput. Electron. Agric., vol. 193, Art. no. 106702, Feb. 2022. doi: 10.1016/j.compag.2022.106702
[52]	Y. Chen, H. Fan, B. Xu, Z. Yan, Y. Kalantidis, M. Rohrbach, Y. Shuicheng, and J. Feng, “Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Seoul, South Korea, 2019, pp. 3434−3443.
[53]	C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 843−852.
[54]	C. Qi, J. Gao, S. Pearson, H. Harman, K. Chen, and L. Shu, “Tea chrysanthemum detection under unstructured environments using the TC-YOLO model,” Expert Syst. Appl., vol. 193, Art. no. 116473, May 2022. doi: 10.1016/j.eswa.2021.116473
[55]	S. Nowozin, B. Cseke, and R. Tomioka, “f-GAN: Training generative neural samplers using variational divergence minimization,” in Proc. 30th Int. Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 271−279.
[56]	A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv: 1511.06434, 2015.
[57]	W. Li, Z. Liang, P. Ma, R. Wang, X. Cui, and P. Chen, “Hausdorff GAN: Improving GAN generation quality with Hausdorff metric,” IEEE Trans. Cybern., vol. 52, no. 10, pp. 10407–10419, Oct. 2022. doi: 10.1109/TCYB.2021.3062396
[58]	J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 2223−2232.
[59]	H. Emami, M. M. Aliabadi, M. Dong, and R. B. Chinnam, “SPA-GAN: Spatial attention GAN for image-to-image translation,” IEEE Trans. Multimedia, vol. 23, pp. 391−401, 2021.
[60]	J. Li, X. Qi, S. H. Nabaei, M. Liu, D. Chen, X. Zhang, X. Yin, and Z. Li, “A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to neural radiance fields (NeRF), 3D Gaussian splatting (3DGS), and beyond,” Plant Phenomics, vol. 7, no. 4, Art. no. 100137, Dec. 2025. doi: 10.1016/j.plaphe.2025.100137
[61]	J. Zhao, W. Ying, Y. Pan, Z. Yi, C. Chen, K. Hu, and H. Kang, “Exploring accurate 3D phenotyping in greenhouse through neural radiance fields,” arXiv preprint arXiv: 2403.15981, 2024.
[62]	L. Jiang, J. Sun, P. W. Chee, C. Li, and L. Fu, “Cotton3DGaussians: Multiview 3D Gaussian splatting for boll mapping and plant architecture analysis,” Comput. Electron. Agric., vol. 234, Art. no. 110293, Jul. 2025. doi: 10.1016/j.compag.2025.110293
[63]	T. Ojo, T. La, A. Morton, and I. Stavness, “Splanting: 3D plant capture with Gaussian splatting,” in Proc. ACM SIGGRAPH Asia Tech. Communications, Tokyo, Japan, 2024, Art. no. 30.
[64]	Y. Bao, T. Ding, J. Huo, Y. Liu, Y. Li, W. Li, Y. Gao, and J. Luo, “3D Gaussian splatting: Survey, technologies, challenges, and opportunities,” IEEE Trans. Circuits Syst. Video Technol., vol. 35, no. 7, pp. 6832–6852, Jul. 2025. doi: 10.1109/TCSVT.2025.3538684
[65]	J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv preprint arXiv: 1804.02767, 2018.
[66]	Z. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 11, pp. 3212–3232, Nov. 2019. doi: 10.1109/TNNLS.2018.2876865
[67]	X. Yi, H. Chen, P. Wu, G. Wang, L. Mo, B. Wu, Y. Yi, X. Fu, and P. Qian, “Light-FC-YOLO: A lightweight method for flower counting based on enhanced feature fusion with a new efficient detection head,” Agronomy, vol. 14, no. 6, Art. no. 1285, Jun. 2024. doi: 10.3390/agronomy14061285
[68]	L. Mohimont, F. Alin, M. Rondeau, N. Gaveau, and L. A. Steffenel, “Computer vision and deep learning for precision viticulture,” Agronomy, vol. 12, no. 10, Art. no. 2463, Oct. 2022.
[69]	S. Liu, X. Li, H. Wu, B. Xin, J. Tang, P. R. Petrie, and M. Whitty, “A robust automated flower estimation system for grape vines,” Biosyst. Eng., vol. 172, pp. 110−123, Aug. 2018.
[70]	M. Hočevar, B. Širok, T. Godeša, and M. Stopar, “Flowering estimation in apple orchards by image analysis,” Precis. Agric., vol. 15, no. 4, pp. 466–478, Aug. 2014. doi: 10.1007/s11119-013-9341-6
[71]	A. Bereciartua-Pérez, L. Gómez, A. Picón, R. Navarra-Mestre, C. Klukas, and T. Eggers, “Insect counting through deep learning-based density maps estimation,” Comput. Electron. Agric., vol. 197, Art. no. 106933, Jun. 2022. doi: 10.1016/j.compag.2022.106933
[72]	S. Bargoti and J. P. Underwood, “Image segmentation for fruit detection and yield estimation in apple orchards,” J. Field Robot., vol. 34, no. 6, pp. 1039–1060, Feb. 2017. doi: 10.1002/rob.21699
[73]	W. Xie, M. Zhao, Y. Liu, D. Yang, K. Huang, C. Fan, and Z. Wang, “Recent advances in transformer technology for agriculture: A comprehensive survey,” Eng. Appl. Artif. Intell., vol. 138, Art. no. 109412, Dec. 2024. doi: 10.1016/j.engappai.2024.109412
[74]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, Ł. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, USA, 2017, pp. 6000−6010
[75]	G. Farjon, O. Krikeb, A. B. Hillel, and V. Alchanatis, “Detection and counting of flowers on apple trees for better chemical thinning decisions,” Precis. Agric., vol. 21, pp. 503–521, Jun. 2020. doi: 10.1007/s11119-019-09679-1
[76]	L. Sun, J. Yao, H. Cao, H. Chen, and G. Teng, “Improved YOLOv5 network for detection of peach blossom quantity,” Agriculture, vol. 14, no. 1, Art. no. 126, Jan. 2024. doi: 10.3390/agriculture14010126
[77]	D. Wang, H. Song, and B. Wang, “YO-AFD: An improved YOLOv8-based deep learning approach for rapid and accurate apple flower detection,” Front. Plant Sci., vol. 16, Art. no. 1541266, Mar. 2025. doi: 10.3389/fpls.2025.1541266
[78]	Y. Yang, G. Zhang, S. Ma, Z. Wang, H. Liu, and S. Gu, “Potted phalaenopsis grading: Precise bloom and bud counting with the PA-YOLO algorithm and multiviewpoint imaging,” Agronomy, vol. 14, no. 1, Art. no. 115, Jan. 2024. doi: 10.3390/agronomy14010115
[79]	K. Xiao, H. Yang, Z. Su, and X. Yang, “Identification and counting of phalaenopsis flowers based on improved YOLOv5,” J. Chin. Agric. Mechanization, vol. 44, no. 11, pp. 155−161, 209, Nov. 2023.
[80]	Q. Wang, J. Gao, W. Lin, and X. Li, “NWPU-Crowd: A large-scale benchmark for crowd counting and localization,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 6, pp. 2141–2149, Jun. 2021. doi: 10.1109/TPAMI.2020.3013269
[81]	V. A. Sindagi, R. Yasarla, and V. M. Patel, “JHU-CROWD++: Large-scale crowd counting dataset and a benchmark method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2594–2609, May 2022. doi: 10.1109/tpami.2020.3035969
[82]	Y.-C. Li, R.-S. Jia, Y.-X. Hu, and H.-M. Sun, “A weakly-supervised crowd density estimation method based on two-stage linear feature calibration,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 965–981, Apr. 2024. doi: 10.1109/JAS.2023.123960
[83]	Y. Ranasinghe, N. G. Nair, W. G. C. Bandara, and V. M. Patel, “CrowdDiff: Multi-hypothesis crowd density estimation using diffusion models,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2024, pp. 12809−12819.
[84]	W. Yuan, W. Hua, P. H. Heinemann, and L. He, “UAV photogrammetry-based apple orchard blossom density estimation and mapping,” Horticulturae, vol. 9, no. 2, Art. no. 266, Feb. 2023. doi: 10.3390/horticulturae9020266
[85]	J. Lin, J. Li, Z. Yang, H. Lu, Y. Ding, and H. Cui, “Estimating litchi flower number using a multicolumn convolutional neural network based on a density map,” Precis. Agric., vol. 23, no. 4, pp. 1226−1247, Jan. 2022.
[86]	U. Bhattarai, S. Bhusal, Q. Zhang, and M. Karkee, “AgRegNet: A deep regression network for flower and fruit density estimation, localization, and counting in orchards,” Comput. Electron. Agric., vol. 227, Art. no. 109534, Dec. 2024. doi: 10.1016/j.compag.2024.109534
[87]	Y. Hnida, M. A. Mahraz, J. Rifff, H. Tairi, and A. Achebour, “Deep learning-based estimation of olive flower density from UAV imagery,” in Proc. Int. Conf. Intelligent Systems and Computer Vision, Fez, Morocco, 2024, pp. 1−6.
[88]	C. Qi, K. Chen, and J. Gao, “A vision transformer-based robotic perception for early tea chrysanthemum flower counting in field environments,” J. Field Robot., vol. 42, no. 1, pp. 65–78, Jan. 2025. doi: 10.1002/rob.22398
[89]	G. Moreira, F. N. dos Santos, and M. Cunha, “Grapevine inflorescence segmentation and flower estimation based on computer vision techniques for early yield assessment,” Smart Agric. Technol., vol. 10, Art. no. 100690, Mar. 2025. doi: 10.1016/j.atech.2024.100690
[90]	Y. Yuan, Y. Wang, and X. Luo, “A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,” IEEE Trans. Neural Networks Learn. Syst., vol. 36, no. 6, pp. 11507–11519, Jun. 2025. doi: 10.1109/TNNLS.2024.3514652
[91]	M. R. Chowdhury, “Scaling a convolutional neural network based flower counting application in a distributed GPU cluster,” Ph.D. dissertation, University of Saskatchewan, Saskatoon, Canada, 2019.
[92]	J. Hobbs, R. Paull, B. Markowicz, and G. Rose, “Flowering density estimation from aerial imagery for automated pineapple flower counting,” in Proc. AI for Social Good Workshop, Cambridge, USA, 2020.
[93]	F. Palacios, G. Bueno, J. Salido, M. P. Diago, I. Hernández, and J. Tardaguila, “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,” Comput. Electron. Agric., vol. 178, Art. no. 105796, Nov. 2020. doi: 10.1016/j.compag.2020.105796
[94]	U. F. Rahim and H. Mineno, “Tomato flower detection and counting in greenhouses using faster region-based convolutional neural network,” J. Image Graphics, vol. 8, no. 4, pp. 107–113, Dec. 2020. doi: 10.18178/joig.8.4.107-113
[95]	Y. Deng, H. Wu, and H. Zhu, “Recognition and counting of citrus flowers based on instance segmentation,” Trans. Chin. Soc. Agric. Eng., vol. 36, no. 7, pp. 200–207, Apr. 2020.
[96]	X. Wang, J. Tang, and M. Whitty, “DeepPhenology: Estimation of apple flower phenology distributions based on deep learning,” Comput. Electron. Agric., vol. 185, Art. no. 106123, Jun. 2021.
[97]	U. F. Rahim, T. Utsumi, and H. Mineno, “Comparison of grape flower counting using patch-based instance segmentation and density-based estimation with convolutional neural networks,” in Proc. SPIE 11884, Int. Symp. Artificial Intelligence and Robotics, Fukuoka, Japan, 2021, Art. no. 118841A.
[98]	K. Tao, A. Wang, Y. Shen, Z. Lu, F. Peng, and X. Wei, “Peach flower density detection based on an improved CNN incorporating attention mechanism and multi-scale feature fusion,” Horticulturae, vol. 8, no. 10, Art. no. 904, Oct. 2022. doi: 10.3390/horticulturae8100904
[99]	U. Bhattarai and M. Karkee, “A weakly-supervised approach for flower/fruit counting in apple orchards,” Comput. Ind., vol. 138, Art. no. 103635, Jun. 2022. doi: 10.1016/j.compind.2022.103635
[100]	Y. Qi, Y. Li, M. Sun, and W. Xu, “Cotton seedling counting algorithm using feature fusion,” Trans. Chin. Soc. Agric. Eng., vol. 38, no. 9, pp. 180–186, May 2022.
[101]	C. Zheng, T. Liu, A. Abd-Elrahman, V. M. Whitaker, and B. Wilkinson, “Object-detection from multi-view remote sensing images: A case study of fruit and flower detection and counting on a central Florida strawberry farm,” Int. J. Appl. Earth Obs. Geoinf., vol. 123, Art. no. 103457, Sep. 2023. doi: 10.1016/j.jag.2023.103457
[102]	J. Li, E. Wang, J. Qiao, Y. Li, L. Li, J. Yao, and G. Liao, “Automatic rape flower cluster counting method based on low-cost labelling and UAV-RGB images,” Plant Methods, vol. 19, no. 1, Art. no. 40, Apr. 2023. doi: 10.1186/s13007-023-01017-x
[103]	A. Jasmine Xavier, S. Valarmathy, J. Gowrishankar, and B. Niranjana Devi, “Multi-class flower counting model with Zha-KNN labelled images using Ma-Yolov9,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 6, pp. 416−426, Jun. 2024.
[104]	Z. Lyu, Y. Wang, C. Huang, G. Zhang, K. Ding, N. Tang, and Z. Zhao, “Dynamic monitoring and counting for lotus flowers and seedpods with UAV based on improved YOLOv7-tiny,” Comput. Electron. Agric., vol. 225, Art. no. 109344, Oct. 2024. doi: 10.1016/j.compag.2024.109344
[105]	J. Lin, J. Li, Z. Ma, C. Li, G. Huang, and H. Lu, “A framework for single-panicle litchi flower counting by regression with multitask learning,” Plant Phenomics, vol. 6, Art. no. 0172, Apr. 2024. doi: 10.34133/plantphenomics.0172
[106]	J. S. Estrada, J. P. Vasconez, L. Fu, and F. A. Cheein, “Deep learning based flower detection and counting in highly populated images: A peach grove case study,” J. Agric. Food Res., vol. 15, Art. no. 100930, Mar. 2024.
[107]	N. Giulietti, S. Tombesi, M. Bedodi, C. Sergenti, M. Carnevale, and H. Giberti, “Hazelnut yield estimation: A vision-based approach for automated counting of hazelnut female flowers,” Sensors, vol. 25, no. 10, Art. no. 3212, May 2025.
[108]	Y. Yue and W. Zhang, “Detection and counting model of soybean at the flowering and podding stage in the field based on improved YOLOv5,” Agriculture, vol. 15, no. 5, Art. no. 528, Feb. 2025. doi: 10.3390/agriculture15050528
[109]	Y. Fang, C. Yang, J. Li, and J. Tu, “SPL-YOLOv8: A lightweight method for rape flower cluster detection and counting based on YOLOv8n,” Algorithms, vol. 18, no. 7, Art. no. 428, Jul. 2025. doi: 10.3390/a18070428
[110]	H. Gan, W. S. Lee, N. Peres, and C. Fraisse, “Development of a multi-angle imaging system for automatic strawberry flower counting,” in Proc. Int. Symp. Artificial Intelligence and Mathematics, Fort Lauderdale, USA, 2020.
[111]	X. Liang, B. Chen, M. Li, C. Wei, and J. Feng, “Method for dynamic counting of cotton rows based on HOG feature and SVM,” Trans. Chin. Soc. Agric. Eng., vol. 36, no. 15, pp. 173–181, Aug. 2020.
[112]	D. J. M. Bos, “Design of a single Kalman filter based on partial detections for automatic flower counting purposes,” M.S. thesis, Eindhoven University of Technology, Eindhoven, Netherlands, 2020.
[113]	W. Houtman, A. Siagkris-Lekkos, D. J. M. Bos, B. J. P. van den Heuvel, M. den Boer, J. Elfring, and M. J. G. van de Molengraft, “Automated flower counting from partial detections: Multiple hypothesis tracking with a connected-flower plant model,” Comput. Electron. Agric., vol. 188, Art. no. 106346, Sep. 2021. doi: 10.1016/j.compag.2021.106346
[114]	C. Tan, C. Li, D. He, and H. Song, “Anchor-free deep convolutional neural network for plant and plant organ detection and counting,” in Proc. ASABE Annu. Int. Meeting, Virtual Event, 2021, Art. no. 2100738.
[115]	S. Chittupalli and Y. K. Chang, “Counting of strawberries and flowers in fields using YOLOv4 and SORT,” in Proc. ASABE Annu. Int. Meeting, Houston, USA, 2022, Art. no. 1.
[116]	C. Zhang, C. Mouton, J. Valente, L. Kooistra, R. van Ooteghem, D. de Hoog, P. van Dalfsen, and P. Frans de Jong, “Automatic flower cluster estimation in apple orchards using aerial and ground based point clouds,” Biosyst. Eng., vol. 221, pp. 164–180, Sep. 2022. doi: 10.1016/j.biosystemseng.2022.05.004
[117]	G. Yu, R. Cai, Y. Luo, M. Hou, and R. Deng, “A-pruning: A lightweight pineapple flower counting network based on filter pruning,” Complex Intell. Syst., vol. 10, no. 2, pp. 2047–2066, Apr. 2024. doi: 10.1007/s40747-023-01261-7
[118]	C. Tan, C. Li, D. He, and H. Song, “Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers,” Comput. Electron. Agric., vol. 215, Art. no. 108359, Dec. 2023. doi: 10.1016/j.compag.2023.108359
[119]	C. Tan, J. Sun, A. H. Paterson, H. Song, and C. Li, “Three-view cotton flower counting through multi-object tracking and RGB-D imagery,” Biosyst. Eng., vol. 246, pp. 233–247, Oct. 2024. doi: 10.1016/j.biosystemseng.2024.08.010
[120]	D. Petti, R. Zhu, S. Li, and C. Li, “Graph neural networks for lightweight plant organ tracking,” Comput. Electron. Agric., vol. 225, Art. no. 109294, Oct. 2024. doi: 10.1016/j.compag.2024.109294
[121]	N. Wang, H. Cao, X. Huang, and M. Ding, “Rapeseed flower counting method based on GhP2-YOLO and StrongSORT algorithm,” Plants, vol. 13, no. 17, Art. no. 2388, Aug. 2024.
[122]	S. Liu, C. Zhou, and W. S. Lee, “Strawberry detection and counting based on YOLOv7 pruning and information based tracking algorithm,” arXiv preprint arXiv: 2407.12614, 2024.
[123]	L. Meyer, A. Gilson, U. Schmid, and M. Stamminger, “FruitNeRF: A unified neural radiance field based fruit counting framework,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Abu Dhabi, United Arab Emirates, 2024, pp. 1−8.
[124]	D. Petti, C. Li, and P. Chee, “Real-time multi-view flower counting with a ground mobile robot,” J. Field Robot., vol. 43, no. 3, pp. 1484–1510, May 2026. doi: 10.1002/rob.70093
[125]	C. Tan, C. Li, and J. Sun, “Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection,” Comput. Electron. Agric., vol. 240, Art. no. 111214, Jan. 2026. doi: 10.1016/j.compag.2025.111214
[126]	Y. Ge, S. Lin, Y. Zhang, Z. Li, H. Cheng, J. Dong, S. Shao, J. Zhang, X. Qi, and Z. Wu, “Tracking and counting of tomato at different growth period using an improving YOLO-Deepsort network for inspection robot,” Machines, vol. 10, no. 6, Art. no. 489, Jun. 2022. doi: 10.3390/machines10060489
[127]	F. He, J. Yue, J. Zhu, A. Seyfried, D. Casas, J. Pettré, and H. Wang, “Learning extremely high density crowds as active matters,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2025, pp. 540−550.
[128]	M. Han, L. Wang, Y. Yuan, and X. Luo, “SGD-DyG: Self-reliant global dependency apprehending on dynamic graphs,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Toronto, Canada, 2025, pp. 802−813.
[129]	X. Liu, G. Li, Y. Qi, Z. Yan, Z. Han, A. van den Hengel, M.-H. Yang, and Q. Huang, “Weakly supervised video individual counting,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2024, pp. 19228−19237.
[130]	S. Zang, L. Shu, R. Han, F. Zhang, Z. Zhang, and D.-J. Deng, “Real-time detection of tea chrysanthemums in complex outdoor scenes,” in Proc. IEEE Int. Conf. Industrial Technology, Wuhan, China, 2025, pp. 1−8.
[131]	J. Kim, S. Chang, and N. Kwak, “PQK: Model compression via pruning, quantization, and knowledge distillation,” in Proc. Annu. Conf. Int. Speech Communication Association (Interspeech), Brno, Czech Republic, 2021, pp. 456−460.
[132]	A. Polino, R. Pascanu, and D. Alistarh, “Model compression via distillation and quantization,” arXiv preprint arXiv: 1802.05668, 2018.
[133]	Y. Tang, Y. Wang, J. Guo, Z. Tu, K. Han, H. Hu, and D. Tao, “A survey on transformer compression,” arXiv preprint arXiv: 2402.05964, 2024.
[134]	X. Qu, D. Aponte, C. Banbury, D. P. Robinson, T. Ding, K. Koishida, I. Zharkov, and T. Chen, “Automatic joint structured pruning and quantization for efficient neural network training and compression,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2025, pp. 15234−15244.
[135]	P. Varshney and M. Pilanci, “Convex distillation: Efficient compression of deep networks via convex optimization,” arXiv preprint arXiv: 2410.06567, 2024.
[136]	D. E. Hernandez, J. R. Chang, and T. E. M. Nordling, “Knowledge distillation: Enhancing neural network compression with integrated gradients,” arXiv preprint arXiv: 2503.13008, 2025.
[137]	H. Shu, W. Li, Y. Tang, Y. Zhang, Y. Chen, H. Li, Y. Wang, and X. Chen, “TinySAM: Pushing the envelope for efficient segment anything model,” in Proc. AAAI Conf. Artif. Intell., vol. 39, no. 19, pp. 20470–20478, Apr. 2025
[138]	M. Wang, S. Wei, J. Shi, Y. Wu, Q. Qu, Y. Zhou, X. Zeng, and B. Tian, “CSR-Net: A novel complex-valued network for fast and precise 3-D microwave sparse reconstruction,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 4476–4492, Aug. 2020. doi: 10.1109/JSTARS.2020.3014696
[139]	S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. 29th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2015, pp. 91−99.
[140]	W. Liu, M. Salzmann, and P. Fua, “Context-aware crowd counting,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 5094−5103.
[141]	W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 1874−1883.
[142]	M. Yang, Z. Li, J. Zhang, L. Qi, and Y. Shi, “Taste more, taste better: Diverse data and strong model boost semi-supervised crowd counting,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2025, pp. 24440−24451.
[143]	H. Meng, X. Hong, Z. Lai, and M. Shang, “Free lunch enhancements for multi-modal crowd counting,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2025, pp. 14013−14023.