[1] |
Pieruschka R, Schurr U. Plant phenotyping: past, present, and future[J]. Plant Phenomics, 2019, 2019: 1-6.
|
[2] |
Nikbakhsh N, Baleghi Y, Agahi H. A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information[J]. Machine Vision and Applications, 2021, 32(5): 1-12.
|
[3] |
Fiorani F, Schurr U. Future scenarios for plant phenotyping[J]. Annual Review of Plant Biology, 2013, 64: 267-291.
|
[4] |
Xia C, Wang L, Chung B K, et al. In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation[J]. Sensors, 2015, 15(8): 20463-20479.
|
[5] |
Grand-Brochier M, Vacavant A, Cerutti G, et al. Tree leaves extraction in natural images: Comparative study of preprocessing tools and segmentation methods[J]. IEEE Transactions on Image Processing, 2015, 24(5): 1549-1560.
|
[6] |
Liu X, Hu C, Li P. Automatic segmentation of overlapped poplar seedling leaves combining Mask R-CNN and DBSCAN[J]. Computers and Electronics in Agriculture, 2020, 178: 105753.
|
[7] |
Brindha G J, Gopi E S. An hierarchical approach for automatic segmentation of leaf images with similar background using kernel smoothing based Gaussian process regression[J]. Ecological Informatics, 2021, 63: 101323.
|
[8] |
Minervini M, Fischbach A, Scharr H, et al. Finely-grained annotated datasets for image-based plant phenotyping[J]. Pattern Recognition Letters, 2015, 81: 80-89.
|
[9] |
Scharr H, Minervini M, Fischbach A, et al. Annotated image datasets of rosette plants[C]//European Conference on Computer Vision, Zürich, Switzerland: Academia, 2014: 6-12.
|
[10] |
Hüther P, Schandry N, Jandrasits K, et al. ARADEEPOPSIS, an automated workflow for top-view plant phenomics using semantic segmentation of leaf states[J]. The Plant Cell, 2020, 32(12): 3674-3688.
|
[11] |
Koornneef M, Meinke D. The development of Arabidopsis as a model plant[J]. The Plant Journal : For Cell and Molecular Biology, 2010, 61(6): 909-921.
|
[12] |
Alonso-Blanco C, Andrade J, Becker C, et al. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana[J]. Cell, 2016, 166(2): 481-491.
|
[13] |
Dellen B, Scharr H, Torras C. Growth signatures of rosette plants from time-lapse video[J]. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(6): 1470-1478.
|
[14] |
Pape J M, Klukas C. Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images[J]. Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), 2015, 3: 1-12.
|
[15] |
Viaud G, Loudet O, Cournède P H. Leaf segmentation and tracking in Arabidopsis thaliana combined to an organ-scale plant model for genotypic differentiation[J]. Frontiers in Plant Science, 2017, 7: 2057.
|
[16] |
Yin X, Liu X, Chen J, et al. Multi-leaf alignment from fluorescence plant images[C]//IEEE Winter Conference on Applications of Computer Vision, Colorado, USA: IEEE, 2014: 437-444.
|
[17] |
Xu L, Li Y, Sun Y, et al. Leaf instance segmentation and counting based on deep object detection and segmentation networks[C]//2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), Toyama, Japan: IEEE, 2018: 180-185.
|
[18] |
Ward D, Moghadam P. Scalable learning for bridging the species gap in image-based plant phenotyping[J]. Computer Vision and Image Understanding, 2020, 197: 103009.
|
[19] |
Kuznichov D, Zvirin A, Honen Y, et al. Data augmentation for leaf segmentation and counting tasks in rosette plants[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, California, USA: IEEE, 2019: 1-10.
|
[20] |
He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]// Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy: IEEE, 2017: 2961-2969.
|
[21] |
Scharr H, Minervini M, French A P, et al. Leaf segmentation in plant phenotyping: A collation study[J]. Machine Vision and Applications, 2016, 27(4): 585-606.
|
[22] |
Giusti A, Cire?an D C, Masci J, et al. Fast image scanning with deep max-pooling convolutional neural networks[C]// 2013 IEEE International Conference on Image Processing, Melbourne, Australia: IEEE, 2013: 4034-4038.
|
[23] |
赵辉,乔艳军,王红君,等. 基于改进YOLOv3的果园复杂环境下苹果果实识别[J]. 农业工程学报,2021,37(16):127-135.Zhao Hui, Qiao Yanjun, Wang Hongjun, et al. Apple fruit recognition in complex orchard environment based on improved YOLOv3[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(16): 127-135. (in Chinese with English abstract)
|
[24] |
Dvornik N, Mairal J, Schmid C. On the importance of visual context for data augmentation in scene understanding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(6): 2014-2028.
|
[25] |
Bloice M D, Stocker C, Holzinger A. Augmentor: An image augmentation library for machine learning[J/OL]. Computer Vision and Pattern Recognition, 2017, [2017-08-11]. https://arxiv.org/abs/1708.04680.
|
[26] |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA: IEEE, 2016: 770-778.
|
[27] |
Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA: IEEE, 2017: 1492-1500.
|
[28] |
Cai Z, Vasconcelos N. Cascade R-CNN: Delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA: IEEE, 2018: 6154-6162.
|
[29] |
燕红文,刘振宇,崔清亮,等. 基于特征金字塔注意力与深度卷积网络的多目标生猪检测[J]. 农业工程学报,2020,36(11):192-202.Yan Hongwen, Liu Zhenyu, Cui Qingliang, et al. Multi-target detection based on feature pyramid attention and deep convolution network for pigs[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(11): 192-202. (in Chinese with English abstract)
|
[30] |
邓颖,吴华瑞,朱华吉. 基于实例分割的柑橘花朵识别及花量统计[J]. 农业工程学报,2020,36(7):200-207.Deng Ying, Wu Huarui, Zhu Huaji. Recognition and counting of citrus flowers based on instance segmentation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(7): 200-207. (in Chinese with English abstract)
|
[31] |
方鹏,郝宏运,李腾飞,等. 基于注意力机制和可变形卷积的鸡只图像实例分割提取[J]. 农业机械学报,2021,52(4):257-265.Fang Peng, Hao Hongyun, Li Tengfei, et al. Instance segmentation of broiler image based on attention mechanism and deformable convolution[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(4): 257-265. (in Chinese with English abstract)
|
[32] |
曹锦纲,杨国田,杨锡运. 基于注意力机制的深度学习路面裂缝检测[J]. 计算机辅助设计与图形学学报,2020,32(8):1324-1333.Cao Jingang, Yang Guotian, Yang Xiyun. Pavement Crack Detection with Deep Learning Based on Attention Mechanism[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(8): 1324-1333. (in Chinese with English abstract)
|
[33] |
Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA: IEEE, 2018: 7132-7141.
|
[34] |
Li R, Wang R, Zhang J, et al. An effective data augmentation strategy for CNN-based pest localization and recognition in the field[J]. IEEE Access, 2019, 7: 160274-160283.
|
[35] |
Chen K, Wang J, Pang J, et al. MMDetection: Open mmlab detection toolbox and benchmark[J/OL]. Computer Vision and Pattern Recognition, 2019, [2019-06-17]. https://arxiv.org/abs/1906.07155.
|
[36] |
Paszke A, Gross S, Massa F, et al. Pytorch: An imperative style, high-performance deep learning library[J]. Advances in Neural Information Processing Systems, 2019, 32: 8026-8037.
|
[37] |
Hanno, LSC as of CVPPP2017: evaluation of the testing data[EB/OL]. 2018, [2018-02-23]. https://competitions. codalab.org/competitions/18405.
|
[38] |
Gomes D P S, Zheng L. Leaf segmentation and counting with deep learning: on model certainty, test-time augmentation, trade-offs[J/OL]. Computer Vision and Pattern Recognition, 2020, [2020-12-21]. https://arxiv.org/abs/2012.11486.
|
[39] |
Ward D, Moghadam P , Hudson N . Deep leaf segmentation using synthetic data[J/OL]. Computer Vision and Pattern Recognition, 2019, [2019-03-21]. https://arxiv.org/abs/1807.10931.
|
[40] |
王志强,钮丹丹,王佳兴,等. 大规模用水节点的灌溉物联网监控系统设计[J]. 排灌机械工程学报,2021,39(10):1062-1067.Wang Zhiqiang, Niu Dandan, Wang Jiaxing, et al. Design of IOT irrigation management system with large-scale nodes[J]. Journal of Drainage and Irrigation Machinery Engineering, 2021, 39(10): 1062-1067. (in Chinese with English abstract)
|