基于深度学习的农业植物表型研究综述
|
摘要
植物表型是指植物可测量的特征和性状,是植物受自身基因表达、环境影响相互作用的结果,也是决定农作物产量、品质和抗逆性等性状的重要因素.大多数植物表型信息可通过数字图像处理的方法获取和分析.随着基因组学研究的快速发展,传统植物表型研究方法在诸多方面已无法满足进一步研究的需要,高精度、高通量的植物表型获取技术成为植物表型研究的新兴热点方向.近年来深度学习在数字图像处理领域取得了突破性进展,在物体识别、分割等应用上,基于深度学习的图像处理在技术表现上远好于传统方法.在植物表型研究领域,如何使用深度学习技术研究植物表型已成为研究人员十分关注的一项研究问题.本综述从植物株型与生理参数获取、植物识别与杂草检测、病虫害检测以及产量预测四个方面,对近几年基于深度学习的植物表型检测方法进行概述,同时还分析了这些方法和传统机器学习方法的优劣,最后对基于深度学习的植物表型研究的未来趋势进行分析和展望.
Plant phenotype refers to measurable traits of plants, which acts as an observable proxy between gene expression and environmental impact, and is also an important determinant for the yield, quality and stress resistance characteristics of crops. Most of the plant phenotypes can be acquired by digital imaging techniques and processed by image processing algorithms. In recent years, the rapid development of genomics advances the study of plant phenotyping in many aspects, especially in terms of high-precision and highthroughput. Traditional plant phenotype research cannot meet these requirements and revolutions are in urgent need. As a breakthrough in computer science, the emergence of deep learning approaches significantly expands the capability of traditional image processing. For instance, state-of-the-art results in identification and segmentation tasks have been achieved by deep-learningbased methods and the records are continually improved by their variants. It is an interesting topic to study how to incorporate deeplearning techniques into plant phenotype research, and various impactful methods have been proposed in the past few years. The objective of this survey is to provide an overview of the current progress of deep-learning-based plant phenotype research in agriculture. In this survey, we elaborate the work from four different aspects,(i) plant morphology and physiological information extraction,(ii) plant identification and weed detection,(iii) pest detection, and(iv) yield prediction. We also analyze the pros and cons of these methods compared to traditional approaches. The potential future trends of plant phenotyping research are discussed at the end of this survey.
Plant phenotype refers to measurable traits of plants, which acts as an observable proxy between gene expression and environmental impact, and is also an important determinant for the yield, quality and stress resistance characteristics of crops. Most of the plant phenotypes can be acquired by digital imaging techniques and processed by image processing algorithms. In recent years, the rapid development of genomics advances the study of plant phenotyping in many aspects, especially in terms of high-precision and highthroughput. Traditional plant phenotype research cannot meet these requirements and revolutions are in urgent need. As a breakthrough in computer science, the emergence of deep learning approaches significantly expands the capability of traditional image processing. For instance, state-of-the-art results in identification and segmentation tasks have been achieved by deep-learningbased methods and the records are continually improved by their variants. It is an interesting topic to study how to incorporate deeplearning techniques into plant phenotype research, and various impactful methods have been proposed in the past few years. The objective of this survey is to provide an overview of the current progress of deep-learning-based plant phenotype research in agriculture. In this survey, we elaborate the work from four different aspects,(i) plant morphology and physiological information extraction,(ii) plant identification and weed detection,(iii) pest detection, and(iv) yield prediction. We also analyze the pros and cons of these methods compared to traditional approaches. The potential future trends of plant phenotyping research are discussed at the end of this survey.
引文
1 Siebner H R, Callicott J H, Sommer T, et al. From the genome to the phenome and back:Linking genes with human brain function and structure using genetically informed neuroimaging. Neuroscience, 2009, 164:1–6
2 Bilder R M, Sabb F W, Cannon T D, et al. Phenomics:The systematic study of phenotypes on a genome-wide scale. Neuroscience, 2009, 164:30 –42
3 Houle D, Govindaraju D R, Omholt S. Phenomics:The next challenge. Nat Rev Genet, 2010, 11:855–866
4 Pan Y H. Analysis of concepts and categories of plant phenome and phenomics(in Chinese). Acta Agronomica Sin, 2015, 41:175–186[潘映红.论植物表型组和植物表型组学的概念与范畴.作物学报. 2015, 41:175–186]
5 Li S K, Zhang X. Information analysis of crop based on multimedia image processing technology(in Chinese). Acta Agronomica Sin, 1998, 24:265 –271[李少昆,张弦.作物株型信息多媒体图像处理技术的研究.作物学报. 1998, 24:265–271]
6 Constantino K P, Gonzales E J, Lazaro L M, et al. Towards an automated plant height measurement and tiller segmentation of rice crops using image processing. In:Mechatronics and Machine Vision in Practice 3. Cham:Springer International Publishing, 2018. 155–168
7 Fang W, Feng H, Yang W N, et al. Phenotypic detection of wheat based on rapid three-dimensional reconstruction(in Chinese). J Agric Sci Tech China, 2016, 18:95–101[方伟,冯慧,杨万能,等.表型检测中用于小麦株型研究的快速三维重建方法.中国农业科技导报. 2016, 18:95–101]
8 Paproki A, Sirault X, Berry S, et al. A novel mesh processing based technique for 3d plant analysis. BMC Plant Biol, 2012, 12:63–75
9 Yao X, Du W, Feng S, et al. Image-based plant nutrient status analysis:An overview. In:IEEE International Conference on Intelligent Computing and Intelligent Systems. Xiamen, 2010. 460–464
10 Singh A, Ganapathysubramanian B, Singh A K, et al. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci, 2016,21 :110–124
11 Cui R X, Liu Y D, Fu J D. Estimation of nitrogen accumulation in winter wheat leaves based on machine learning and visible spectrum(in Chinese). Spectrosc Spect Anal, 2016, 36:1837–1842[崔日鲜,刘亚东,付金东.基于机器学习和可见光光谱的冬小麦叶片氮积累量估算.光谱学与光谱分析, 2016, 36:1837–1842]
12 Zhang Y R, Chen S S, Zhou X Q. Wheat moisture content identification based on image processing technology(in Chinese). J Henan Univ Tech-Nat Sci Ed, 2014, 35:101–106[张玉荣,陈赛赛,周显青.基于图像处理的小麦水分含量识别方法研究.河南工业大学学报(自然科学版), 2014, 35:101–106]
13 Burks T F, Shearer S A, Gates R S, et al. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans ASAE, 2000, 43:1029–1037
14 Hussin N A C, Jamil N, Nordin S, et al. Plant species identification by using scale invariant feature transform(sift)and grid based colour moment(gbcm). In:2013 IEEE Conference on Open Systems(ICOS). New York:IEEE, 2013. 226–230
15 Wang X F, Huang D S, Du J X, et al. Classification of plant leaf images with complicated background. Appl Math Comput, 2008, 205:916–926
16 Strange R N, Scott P R. Plant disease:A threat to global food security. Annu Rev Phytopathol, 2005, 43:83–116
17 Geiger F, Bengtsson J, Berendse F, et al. Persistent negative effects of pesticides on biodiversity and biological control potential on european farmland. Basic Appl Ecol, 2010, 11:97–105
18 Barbedo J G A. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis, 2014, 98:1709–1716
19 Atoum Y, Afridi M J, Liu X, et al. On developing and enhancing plant-level disease rating systems in real fields. Pattern Recognit, 2016, 53:287 –299
20 Qin F, Liu D, Sun B, et al. Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE, 2016, 11:e0168274
21 Al-Hiary H, Bani-Ahmad S, Reyalat M, et al. Fast and accurate detection and classification of plant diseases. Int J Comput Appl, 2011, 17:31–38
22 Omrani E, Khoshnevisan B, Shamshirband S, et al. Potential of radial basis function-based support vector regression for apple disease detection.Measurement, 2014, 55:512–519
23 Hernández Rabadán D L, Ramos Quintana F, Guerrero Juk J. Integrating soms and a bayesian classifier for segmenting diseased plants in uncontrolled environments. Sci World J, 2014, 214674
24 Correa-Tome F E, Sanchez-Yanez R E, Ayala-Ramirez V. Comparison of perceptual color spaces for natural image segmentation tasks. Opt Eng,2011, 50:117203
25 Schikora M, Schikora A, Kogel K H, et al. Probabilistic classification of disease symptoms caused by salmonella on Arabidopsis plants. GI Jahrestagung, 2010, 176:874–879
26 Barbedo J G A. A new automatic method for disease symptom segmentation in digital photographs of plant leaves. Eur J Plant Pathol, 2017,147 :349–364
27 Clément A, Verfaille T, Lormel C, et al. A new colour vision system to quantify automatically foliar discolouration caused by insect pests feeding on leaf cells. Biosyst Eng, 2015, 133:128–140
28 Mohan K J, Balasubramanian M, Palanivel S. Detection and recognition of diseases from paddy plant leaf images. In J Comput Appl, 2016, 144:34 –41
29 Barbedo J G A. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng, 2016,144 :52–60
30 Liu Z, Huang J, Tao R, et al. Estimating the severity of rice brown spot disease based on principal component analysis and radial basis function neural network. Spectro Spect Anal, 2008, 28:2156–2160
31 Liu Z Y, Wu H F, Huang J F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Comput Electron Agric, 2010, 72:99–106
32 Wang Q P. Methods for measuring yield of several main fruit species’s trees in orchard(in Chinese). Kexue Zhongyang, 2014, 2014:23[王秋萍.几种主要果树的果园测产方法.科学种养, 2014, 2014:23]
33 Elkind A, Ablimit, Sun Z M, et al. Measurement of fruit tree yield in Kashgar Area(in Chinese). For Xinjiang, 2013, 2013:30–31[艾尔肯·亚生,阿不力米提,孙芝梅,等.喀什地区果树测产方法.新疆林业, 2013, 2013:30–31]
34 Germain C, Rousseaud R, Grenier G. Non destructive counting of wheatear with picture analysis. In:Fifth International Conference on Image Processing and its Applications, London:IEE, 1995. 435–439
35 Duan L, Huang C, Chen G, et al. Determination of rice panicle numbers during heading by multi-angle imaging. Crop J, 2015, 3:211–219
36 Guérin D, Cointault F, Gée C, et al. Feasibility study of a wheatears counting vision system. CSIMTA. 2004, 5:658–664
37 Fan M Y, Ma Q, Liu J M, et al. Wheat ear counting method in field environment based on computer vision(in Chinese). Trans Chin Soc Agric Mach, 2015, 46:234–239[范梦扬,马钦,刘峻明,等.基于机器视觉的大田环境小麦麦穗计数方法.农业机械学报. 2015, 46:234–239]
38 Liu T, Sun C M, Wang L J, et al. Wheat ear counting method based on image processing technology(in Chinese). Trans Chin Soc Agric Mach,2014, 45:282–290[刘涛,孙成明,王力坚,等.基于图像处理技术的大田麦穗计数.农业机械学报. 2014, 45:282-–290]
39 Cointault F, Guerin D, Guillemin J, et al. In-field Triticum aestivum ear counting using colour-texture image analysis. New Zeal J Crop Hortic Sci, 2008, 36:117–130
40 Cointault F, Journaux L, Miteran J, et al. Improvements of image processing for wheat ear counting. In:International Conference on Agricultural Engineering. Silsoe, UK:European Society of Agricultural Engineers(AgEng), 2008. 1–11
41 Frédéric C, Ludovic J, Gilles R, et al. Texture, color and frequential proxy-detection image processing for crop characterization in a context of precision agriculture. Agric Sci, 2012, 1:49–70
42 Journaux L, Marin A, Cointault F, et al. Fourier filtering for wheat detection in a context of yield prediction. In:17th World Congress of the International Commission of Agricultural and Biosystems Engineering(CIGR). Quebec, 2010. 1–19
43 Fernandez-Gallego J A, Kefauver S C, Gutiérrez N A, et al. Wheat ear counting in-field conditions:High throughput and low-cost approach using rgb images. Plant Method, 2018, 14:22–33
44 Sadeghi-Tehran P, Sabermanesh K, Virlet N, et al. Automated method to determine two critical growth stages of wheat:Heading and flowering.Front Plant Sci, 2017, 8:252–265
45 Du S W, Li Y N, Yao M, et al. Grain counting method based on image segmentation of wheat spikelets(in Chinese). J Nanjing Agric Univ, 2018,41 :742–751[杜世伟,李毅念,姚敏,等.基于小麦穗部小穗图像分割的籽粒计数方法.南京农业大学学报. 2018, 41:742–751]
46 Zhao J, Tow J, Katupitiya J. On-tree fruit recognition using texture properties and color data. In:IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE, 2005. 263–268
47 Hayashi S, Ota T, Kubota K, et al. Robotic harvesting technology for fruit vegetables in protected horticultural production. In:Antony ed..Information and Technology for Sustainable Fruit and Vegetable Production. Montpellier:Cemagref, 2005. 227–236
48 Hannan M, Burks T, Bulanon D M. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection.Agric Eng Inter:CIGR J, 2010, 14:1–17
49 Seng W C, Mirisaee S H. A new method for fruits recognition system. In:Nordin M J, Jumari K, eds. International Conference on Electrical Engineering and Informatics. New York:IEEE, 2009. 130–134
50 Lak M B, Minaei S, Amiriparian J, et al. Apple fruits recognition under natural luminance using machine vision. Adv J Food Sci Tech, 2010, 2:325 –327
51 Arivazhagan S, Shebiah R N, Nidhyanandhan S S, et al. Fruit recognition using color and texture features. J Emerg Trends Comput Inf Sci,2010, 1:90–94
52 Patel H N, Jain R, Joshi M V. Fruit detection using improved multiple features based algorithm. Int J Comput Appl, 2011, 13:1–5
53 Dorj U O, Lee K, Lee M. A computer vision algorithm for tangerine yield estimation. Int J Bio-Sci Bio-Tech, 2013, 5:101–110
54 Guo W, Potgieter A, Jordan D, et al. Automatic detecting and counting of sorghum heads in breeding field using rgb imagery from UAV. In:Claus Gr?n S, Gerrit-Jan C, eds. CIGR-AgEng. Aarhus, 2016. 1–5
55 Guo W, Rage U K, Ninomiya S. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model.Comput Electron Agric, 2013, 96:58–66
56 Patel H, Jain R, Joshi M. Automatic segmentation and yield measurement of fruit using shape analysis. Int J Comput Appl, 2012, 45:19–24
57 Parrish E, Goksel A. Pictorial pattern recognition applied to fruit harvesting. Trans ASAE, 1977, 20:822–827
58 Wang Q, Nuske S, Bergerman M, et al. Automated crop yield estimation for apple orchards. In:Desai J P, Dudek G, eds. The 13th International Symposium on Experimental Robotics. Québec, 2013. Heidelberg:Springer, 2013. 745–758
59 Linker R, Cohen O, Naor A. Determination of the number of green apples in rgb images recorded in orchards. Comput Electron Agric, 2012, 81:45 –57
60 Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol, 1962, 160:106–154
61 Fukushima K. A hierarchical neural network model for associative memory. Biol Cybern, 1984, 50:105–113
62 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86:2278–2324
63 Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. Commun ACM, 2017, 60:84–90
64 Deng J, Dong W, Socher R, et al. Imagenet:A large-scale hierarchical image database. In:IEEE. IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops. New York:IEEE, 2009. 248–255
65 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Sci, 2014
66 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2015. 1–9
67 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2016. 770–778
68 Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39:640–651
69 Badrinarayanan V, Kendall A, Cipolla R. Segnet:A deep convolutional encoder-decoder architecture for image segmentation. In:IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39:2481–2495
70 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In:27th IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New York:IEEE, 2014. 580–587
71 Girshick R. Fast R-CNN. In:Proceedings of the IEEE International Conference on Computer Vision. New York:IEEE, 2015. 1440–1448
72 Ren S, He K, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39:1137–1149
73 Farnsworth E J, Chu M, Kress W J, et al. Next-generation field guides. Bioscience, 2013, 63:891–899
74 Elphick C S. How you count counts:The importance of methods research in applied ecology. J Appl Ecol, 2008, 45:1313–1320
75 Austen G E, Bindemann M, Griffiths R A, et al. Species identification by experts and non-experts:Comparing images from field guides. Sci Rep, 2016, 6:33634
76 Kumar N, Belhumeur P N, Biswas A, et al. Leafsnap:A computer vision system for automatic plant species identification. In:Fitzgibbon A,Lazebnik S, eds. 12th European Conference on Computer Vision(ECCV), Berlin, 2012. Berlin:Springer Berlin Heidelberg, 2012. 502–516
77 Joly A, Go?au H, Bonnet P, et al. Interactive plant identification based on social image data. Ecol Inf, 2014, 23:22–34
78 Lee S H, Chan C S, Wilkin P, et al. Deep-plant:Plant identification with convolutional neural networks. In:IEEE International Conference on Image Processing(ICIP). New York:IEEE, 2015. 452–456
79 Zeiler M D, Taylor G W, Fergus R. Adaptive deconvolutional networks for mid and high level feature learning. In:International Conference on Computer Vision. New York:IEEE, 2011. 2018–2025
80 Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In:Fleet D, Pajdla T, eds. 13th European Conference on Computer Vision(ECCV). Cham:Springer International Publishing, 2014. 818–833
81 Grinblat G L, Uzal L C, Larese M G, et al. Deep learning for plant identification using vein morphological patterns. Comput Electron Agric,2016, 127:418–424
82 Joly A, Go?au H, Glotin H, et al. Lifeclef 2016:Multimedia life species identification challenges. In:Fuhr N, Quaresma P, eds. 7th International Conference of the CLEF-Association(CLEF). Cham:Springer, 2016. 286–310
83 Goeau H, Bonnet P, Joly A. Plant identification based on noisy web data:The amazing performance of deep learning(lifeclef 2017). In:Gareth J F J, Séamus L, eds. CLEF 2017-Conference and Labs of the Evaluation Forum. Cham:Springer International Publishing, 2017. 1–13
84 Lasseck M. Image-based plant species identification with deep convolutional neural networks. In:Cappellato L, Ferro N, eds. Conference and Labs of the Evaluation Forum(CLEF)(Working Notes). Aachen:CEUR-WS, 2017. 1–11
85 Sun Y, Liu Y, Wang G, et al. Deep learning for plant identification in natural environment. Comput Intell Neurosci, 2017, 2017:1–6
86 J?rgensen L N, Noe E, Langvad A M, et al. Vurdering af plantev?rn onlines?konomiske og milj?m?ssige effekt. K?benhavn:Forlag Milj?styrelsen, 2007. 1–246
87 Dyrmann M, Karstoft H, Midtiby H S. Plant species classification using deep convolutional neural network. Biosyst Eng, 2016, 151:72–80
88 Potena C, Nardi D, Pretto A. Fast and accurate crop and weed identification with summarized train sets for precision agriculture. In:Chen W,Hosoda K, eds. Intelligent Autonomous Systems 14. Cham:Springer International Publishing, 2017. 105–121
89 Milioto A, Lottes P, Stachniss C. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNS. In:IEEE International Conference on Robotics and Automation(ICRA). Los Alamitos:IEEE, 2018. 2229–2235
90 Lottes P, Behley J, Milioto A, et al. Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. IEEE Robot Autom Lett, 2018, 3:2870–2877
91 Sa I, Popovi?M, Khanna R, et al. Weedmap:A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens, 2018, 10:1423–1445
92 Mohanty S P, Hughes D P, SalathéM. Using deep learning for image-based plant disease detection. Front Plant Sci, 2016, 7:1–10
93 Brahimi M, Boukhalfa K, Moussaoui A. Deep learning for tomato diseases:Classification and symptoms visualization. Appl Artif Intel, 2017,31 :299–315
94 Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. In:Mitschang B, Nicklas D, eds. The17 th Conference on Database Systems for Business, Technology, and Web. Stuttgart:Gesellschaft für Informatik e.V., 2017. 79–88
95 Oppenheim D, Shani G. Potato disease classification using convolution neural networks. Adv Anim Biosci, 2017, 8:244–249
96 Too E C, Yujian L, Njuki S, et al. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric, 2018, doi:10.1016/j.compag.2018.03.032
97 Liu B, Zhang Y, He D J, et al. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 2018, 10:11
98 Kawasaki Y, Uga H, Kagiwada S, et al. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In:Bebis G, Boyle R, eds. Advances in Visual Computing. Cham:Springer International Publishing, 2015. 638–645
99 Fujita E, Kawasaki Y, Uga H, et al. Basic investigation on a robust and practical plant diagnostic system. In:15th IEEE International Conference on Machine Learning and Applications(ICMLA). New York:IEEE, 2016. 989–992
100 Fuentes A, Yoon S, Kim S C, et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors,2017, 17:2022–2042
101 Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci, 2017, 2017:1–8
102 Ramcharan A, McCloskey P, Baranowski K, et al. Assessing a mobile-based deep learning model for plant disease surveillance. Comput Sci,2018, 1–13
103 Picon A, Alvarez-Gila A, Seitz M, et al. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agri, 2018, doi:10.1016/j.compag.2018.04.002
104 Johannes A, Picon A, Alvarez-Gila A, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case.Comput Electron Agri, 2017, 138:200–209
105 Li Y, Cui Z, Ni Y, et al. Plant density effect on grain number and weight of two winter wheat cultivars at different spikelet and grain positions.PLoS ONE, 2016, 11:e0155351
106 Pound MP, Atkinson J A, Wells D M, et al. Deep learning for multi-task plant phenotyping. In:16th IEEE International Conference on Computer Vision(ICCV)Venice. New York:IEEE, 2017. 2055–2063
107 Madec S, Jin X, Lu H, et al. Ear density estimation from high resolution rgb imagery using deep learning technique. Agric For Meteorol, 2019,264 :225–234
108 Lu H, Cao Z, Xiao Y, et al. Tasselnet:Counting maize tassels in the wild via local counts regression network. Plant Method, 2017, 13:79–92
109 Kuwata K, Shibasaki R. Estimating crop yields with deep learning and remotely sensed data. In:IEEE International Geoscience and Remote Sensing Symposium(IGARSS). New York:IEEE, 2015. 858–861
110 You J, Li X, Low M, et al. Deep gaussian process for crop yield prediction based on remote sensing data. In:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2017. 4559–4566
111 Chen S W, Shivakumar S S, Dcunha S, et al. Counting apples and oranges with deep learning:A data-driven approach. IEEE Robot Autom Lett,2017, 2:781–788
112 Pothen Z S, Nuske S. Texture-based fruit detection via images using the smooth patterns on the fruit. In:Okamura A, Menciassi A, eds. 2016IEEE International Conference on Robotics and Automation(ICRA). New York:IEEE, 2016. 5171–5176
113 Rahnemoonfar M, Sheppard C. Deep count:Fruit counting based on deep simulated learning. Sensors, 2017, 17:905–916
114 Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning. In:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, AAAI, 2017. 12–18
115 Bargoti S, Underwood J P. Image segmentation for fruit detection and yield estimation in apple orchards. J Field Robotics, 2017, 34:1039–1060
116 Hung C, Underwood J, Nieto J, et al. A feature learning based approach for automated fruit yield estimation. In:Mejias L, Corke P, eds. The 9th Conference on Field and Service Robotics. Cham:Springer International Publishing, 2015. 485–498
117 Roerdink J B, Meijster A. The watershed transform:Definitions, algorithms and parallelization strategies. Fund Inform, 2000, 41:187–228
118 Atherton T J, Kerbyson D J. Size invariant circle detection. Image Vision Comput, 1999, 17:795–803
2 Bilder R M, Sabb F W, Cannon T D, et al. Phenomics:The systematic study of phenotypes on a genome-wide scale. Neuroscience, 2009, 164:30 –42
3 Houle D, Govindaraju D R, Omholt S. Phenomics:The next challenge. Nat Rev Genet, 2010, 11:855–866
4 Pan Y H. Analysis of concepts and categories of plant phenome and phenomics(in Chinese). Acta Agronomica Sin, 2015, 41:175–186[潘映红.论植物表型组和植物表型组学的概念与范畴.作物学报. 2015, 41:175–186]
5 Li S K, Zhang X. Information analysis of crop based on multimedia image processing technology(in Chinese). Acta Agronomica Sin, 1998, 24:265 –271[李少昆,张弦.作物株型信息多媒体图像处理技术的研究.作物学报. 1998, 24:265–271]
6 Constantino K P, Gonzales E J, Lazaro L M, et al. Towards an automated plant height measurement and tiller segmentation of rice crops using image processing. In:Mechatronics and Machine Vision in Practice 3. Cham:Springer International Publishing, 2018. 155–168
7 Fang W, Feng H, Yang W N, et al. Phenotypic detection of wheat based on rapid three-dimensional reconstruction(in Chinese). J Agric Sci Tech China, 2016, 18:95–101[方伟,冯慧,杨万能,等.表型检测中用于小麦株型研究的快速三维重建方法.中国农业科技导报. 2016, 18:95–101]
8 Paproki A, Sirault X, Berry S, et al. A novel mesh processing based technique for 3d plant analysis. BMC Plant Biol, 2012, 12:63–75
9 Yao X, Du W, Feng S, et al. Image-based plant nutrient status analysis:An overview. In:IEEE International Conference on Intelligent Computing and Intelligent Systems. Xiamen, 2010. 460–464
10 Singh A, Ganapathysubramanian B, Singh A K, et al. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci, 2016,21 :110–124
11 Cui R X, Liu Y D, Fu J D. Estimation of nitrogen accumulation in winter wheat leaves based on machine learning and visible spectrum(in Chinese). Spectrosc Spect Anal, 2016, 36:1837–1842[崔日鲜,刘亚东,付金东.基于机器学习和可见光光谱的冬小麦叶片氮积累量估算.光谱学与光谱分析, 2016, 36:1837–1842]
12 Zhang Y R, Chen S S, Zhou X Q. Wheat moisture content identification based on image processing technology(in Chinese). J Henan Univ Tech-Nat Sci Ed, 2014, 35:101–106[张玉荣,陈赛赛,周显青.基于图像处理的小麦水分含量识别方法研究.河南工业大学学报(自然科学版), 2014, 35:101–106]
13 Burks T F, Shearer S A, Gates R S, et al. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans ASAE, 2000, 43:1029–1037
14 Hussin N A C, Jamil N, Nordin S, et al. Plant species identification by using scale invariant feature transform(sift)and grid based colour moment(gbcm). In:2013 IEEE Conference on Open Systems(ICOS). New York:IEEE, 2013. 226–230
15 Wang X F, Huang D S, Du J X, et al. Classification of plant leaf images with complicated background. Appl Math Comput, 2008, 205:916–926
16 Strange R N, Scott P R. Plant disease:A threat to global food security. Annu Rev Phytopathol, 2005, 43:83–116
17 Geiger F, Bengtsson J, Berendse F, et al. Persistent negative effects of pesticides on biodiversity and biological control potential on european farmland. Basic Appl Ecol, 2010, 11:97–105
18 Barbedo J G A. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis, 2014, 98:1709–1716
19 Atoum Y, Afridi M J, Liu X, et al. On developing and enhancing plant-level disease rating systems in real fields. Pattern Recognit, 2016, 53:287 –299
20 Qin F, Liu D, Sun B, et al. Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE, 2016, 11:e0168274
21 Al-Hiary H, Bani-Ahmad S, Reyalat M, et al. Fast and accurate detection and classification of plant diseases. Int J Comput Appl, 2011, 17:31–38
22 Omrani E, Khoshnevisan B, Shamshirband S, et al. Potential of radial basis function-based support vector regression for apple disease detection.Measurement, 2014, 55:512–519
23 Hernández Rabadán D L, Ramos Quintana F, Guerrero Juk J. Integrating soms and a bayesian classifier for segmenting diseased plants in uncontrolled environments. Sci World J, 2014, 214674
24 Correa-Tome F E, Sanchez-Yanez R E, Ayala-Ramirez V. Comparison of perceptual color spaces for natural image segmentation tasks. Opt Eng,2011, 50:117203
25 Schikora M, Schikora A, Kogel K H, et al. Probabilistic classification of disease symptoms caused by salmonella on Arabidopsis plants. GI Jahrestagung, 2010, 176:874–879
26 Barbedo J G A. A new automatic method for disease symptom segmentation in digital photographs of plant leaves. Eur J Plant Pathol, 2017,147 :349–364
27 Clément A, Verfaille T, Lormel C, et al. A new colour vision system to quantify automatically foliar discolouration caused by insect pests feeding on leaf cells. Biosyst Eng, 2015, 133:128–140
28 Mohan K J, Balasubramanian M, Palanivel S. Detection and recognition of diseases from paddy plant leaf images. In J Comput Appl, 2016, 144:34 –41
29 Barbedo J G A. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng, 2016,144 :52–60
30 Liu Z, Huang J, Tao R, et al. Estimating the severity of rice brown spot disease based on principal component analysis and radial basis function neural network. Spectro Spect Anal, 2008, 28:2156–2160
31 Liu Z Y, Wu H F, Huang J F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Comput Electron Agric, 2010, 72:99–106
32 Wang Q P. Methods for measuring yield of several main fruit species’s trees in orchard(in Chinese). Kexue Zhongyang, 2014, 2014:23[王秋萍.几种主要果树的果园测产方法.科学种养, 2014, 2014:23]
33 Elkind A, Ablimit, Sun Z M, et al. Measurement of fruit tree yield in Kashgar Area(in Chinese). For Xinjiang, 2013, 2013:30–31[艾尔肯·亚生,阿不力米提,孙芝梅,等.喀什地区果树测产方法.新疆林业, 2013, 2013:30–31]
34 Germain C, Rousseaud R, Grenier G. Non destructive counting of wheatear with picture analysis. In:Fifth International Conference on Image Processing and its Applications, London:IEE, 1995. 435–439
35 Duan L, Huang C, Chen G, et al. Determination of rice panicle numbers during heading by multi-angle imaging. Crop J, 2015, 3:211–219
36 Guérin D, Cointault F, Gée C, et al. Feasibility study of a wheatears counting vision system. CSIMTA. 2004, 5:658–664
37 Fan M Y, Ma Q, Liu J M, et al. Wheat ear counting method in field environment based on computer vision(in Chinese). Trans Chin Soc Agric Mach, 2015, 46:234–239[范梦扬,马钦,刘峻明,等.基于机器视觉的大田环境小麦麦穗计数方法.农业机械学报. 2015, 46:234–239]
38 Liu T, Sun C M, Wang L J, et al. Wheat ear counting method based on image processing technology(in Chinese). Trans Chin Soc Agric Mach,2014, 45:282–290[刘涛,孙成明,王力坚,等.基于图像处理技术的大田麦穗计数.农业机械学报. 2014, 45:282-–290]
39 Cointault F, Guerin D, Guillemin J, et al. In-field Triticum aestivum ear counting using colour-texture image analysis. New Zeal J Crop Hortic Sci, 2008, 36:117–130
40 Cointault F, Journaux L, Miteran J, et al. Improvements of image processing for wheat ear counting. In:International Conference on Agricultural Engineering. Silsoe, UK:European Society of Agricultural Engineers(AgEng), 2008. 1–11
41 Frédéric C, Ludovic J, Gilles R, et al. Texture, color and frequential proxy-detection image processing for crop characterization in a context of precision agriculture. Agric Sci, 2012, 1:49–70
42 Journaux L, Marin A, Cointault F, et al. Fourier filtering for wheat detection in a context of yield prediction. In:17th World Congress of the International Commission of Agricultural and Biosystems Engineering(CIGR). Quebec, 2010. 1–19
43 Fernandez-Gallego J A, Kefauver S C, Gutiérrez N A, et al. Wheat ear counting in-field conditions:High throughput and low-cost approach using rgb images. Plant Method, 2018, 14:22–33
44 Sadeghi-Tehran P, Sabermanesh K, Virlet N, et al. Automated method to determine two critical growth stages of wheat:Heading and flowering.Front Plant Sci, 2017, 8:252–265
45 Du S W, Li Y N, Yao M, et al. Grain counting method based on image segmentation of wheat spikelets(in Chinese). J Nanjing Agric Univ, 2018,41 :742–751[杜世伟,李毅念,姚敏,等.基于小麦穗部小穗图像分割的籽粒计数方法.南京农业大学学报. 2018, 41:742–751]
46 Zhao J, Tow J, Katupitiya J. On-tree fruit recognition using texture properties and color data. In:IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE, 2005. 263–268
47 Hayashi S, Ota T, Kubota K, et al. Robotic harvesting technology for fruit vegetables in protected horticultural production. In:Antony ed..Information and Technology for Sustainable Fruit and Vegetable Production. Montpellier:Cemagref, 2005. 227–236
48 Hannan M, Burks T, Bulanon D M. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection.Agric Eng Inter:CIGR J, 2010, 14:1–17
49 Seng W C, Mirisaee S H. A new method for fruits recognition system. In:Nordin M J, Jumari K, eds. International Conference on Electrical Engineering and Informatics. New York:IEEE, 2009. 130–134
50 Lak M B, Minaei S, Amiriparian J, et al. Apple fruits recognition under natural luminance using machine vision. Adv J Food Sci Tech, 2010, 2:325 –327
51 Arivazhagan S, Shebiah R N, Nidhyanandhan S S, et al. Fruit recognition using color and texture features. J Emerg Trends Comput Inf Sci,2010, 1:90–94
52 Patel H N, Jain R, Joshi M V. Fruit detection using improved multiple features based algorithm. Int J Comput Appl, 2011, 13:1–5
53 Dorj U O, Lee K, Lee M. A computer vision algorithm for tangerine yield estimation. Int J Bio-Sci Bio-Tech, 2013, 5:101–110
54 Guo W, Potgieter A, Jordan D, et al. Automatic detecting and counting of sorghum heads in breeding field using rgb imagery from UAV. In:Claus Gr?n S, Gerrit-Jan C, eds. CIGR-AgEng. Aarhus, 2016. 1–5
55 Guo W, Rage U K, Ninomiya S. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model.Comput Electron Agric, 2013, 96:58–66
56 Patel H, Jain R, Joshi M. Automatic segmentation and yield measurement of fruit using shape analysis. Int J Comput Appl, 2012, 45:19–24
57 Parrish E, Goksel A. Pictorial pattern recognition applied to fruit harvesting. Trans ASAE, 1977, 20:822–827
58 Wang Q, Nuske S, Bergerman M, et al. Automated crop yield estimation for apple orchards. In:Desai J P, Dudek G, eds. The 13th International Symposium on Experimental Robotics. Québec, 2013. Heidelberg:Springer, 2013. 745–758
59 Linker R, Cohen O, Naor A. Determination of the number of green apples in rgb images recorded in orchards. Comput Electron Agric, 2012, 81:45 –57
60 Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol, 1962, 160:106–154
61 Fukushima K. A hierarchical neural network model for associative memory. Biol Cybern, 1984, 50:105–113
62 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86:2278–2324
63 Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. Commun ACM, 2017, 60:84–90
64 Deng J, Dong W, Socher R, et al. Imagenet:A large-scale hierarchical image database. In:IEEE. IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops. New York:IEEE, 2009. 248–255
65 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Sci, 2014
66 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2015. 1–9
67 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2016. 770–778
68 Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39:640–651
69 Badrinarayanan V, Kendall A, Cipolla R. Segnet:A deep convolutional encoder-decoder architecture for image segmentation. In:IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39:2481–2495
70 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In:27th IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New York:IEEE, 2014. 580–587
71 Girshick R. Fast R-CNN. In:Proceedings of the IEEE International Conference on Computer Vision. New York:IEEE, 2015. 1440–1448
72 Ren S, He K, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39:1137–1149
73 Farnsworth E J, Chu M, Kress W J, et al. Next-generation field guides. Bioscience, 2013, 63:891–899
74 Elphick C S. How you count counts:The importance of methods research in applied ecology. J Appl Ecol, 2008, 45:1313–1320
75 Austen G E, Bindemann M, Griffiths R A, et al. Species identification by experts and non-experts:Comparing images from field guides. Sci Rep, 2016, 6:33634
76 Kumar N, Belhumeur P N, Biswas A, et al. Leafsnap:A computer vision system for automatic plant species identification. In:Fitzgibbon A,Lazebnik S, eds. 12th European Conference on Computer Vision(ECCV), Berlin, 2012. Berlin:Springer Berlin Heidelberg, 2012. 502–516
77 Joly A, Go?au H, Bonnet P, et al. Interactive plant identification based on social image data. Ecol Inf, 2014, 23:22–34
78 Lee S H, Chan C S, Wilkin P, et al. Deep-plant:Plant identification with convolutional neural networks. In:IEEE International Conference on Image Processing(ICIP). New York:IEEE, 2015. 452–456
79 Zeiler M D, Taylor G W, Fergus R. Adaptive deconvolutional networks for mid and high level feature learning. In:International Conference on Computer Vision. New York:IEEE, 2011. 2018–2025
80 Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In:Fleet D, Pajdla T, eds. 13th European Conference on Computer Vision(ECCV). Cham:Springer International Publishing, 2014. 818–833
81 Grinblat G L, Uzal L C, Larese M G, et al. Deep learning for plant identification using vein morphological patterns. Comput Electron Agric,2016, 127:418–424
82 Joly A, Go?au H, Glotin H, et al. Lifeclef 2016:Multimedia life species identification challenges. In:Fuhr N, Quaresma P, eds. 7th International Conference of the CLEF-Association(CLEF). Cham:Springer, 2016. 286–310
83 Goeau H, Bonnet P, Joly A. Plant identification based on noisy web data:The amazing performance of deep learning(lifeclef 2017). In:Gareth J F J, Séamus L, eds. CLEF 2017-Conference and Labs of the Evaluation Forum. Cham:Springer International Publishing, 2017. 1–13
84 Lasseck M. Image-based plant species identification with deep convolutional neural networks. In:Cappellato L, Ferro N, eds. Conference and Labs of the Evaluation Forum(CLEF)(Working Notes). Aachen:CEUR-WS, 2017. 1–11
85 Sun Y, Liu Y, Wang G, et al. Deep learning for plant identification in natural environment. Comput Intell Neurosci, 2017, 2017:1–6
86 J?rgensen L N, Noe E, Langvad A M, et al. Vurdering af plantev?rn onlines?konomiske og milj?m?ssige effekt. K?benhavn:Forlag Milj?styrelsen, 2007. 1–246
87 Dyrmann M, Karstoft H, Midtiby H S. Plant species classification using deep convolutional neural network. Biosyst Eng, 2016, 151:72–80
88 Potena C, Nardi D, Pretto A. Fast and accurate crop and weed identification with summarized train sets for precision agriculture. In:Chen W,Hosoda K, eds. Intelligent Autonomous Systems 14. Cham:Springer International Publishing, 2017. 105–121
89 Milioto A, Lottes P, Stachniss C. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNS. In:IEEE International Conference on Robotics and Automation(ICRA). Los Alamitos:IEEE, 2018. 2229–2235
90 Lottes P, Behley J, Milioto A, et al. Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. IEEE Robot Autom Lett, 2018, 3:2870–2877
91 Sa I, Popovi?M, Khanna R, et al. Weedmap:A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens, 2018, 10:1423–1445
92 Mohanty S P, Hughes D P, SalathéM. Using deep learning for image-based plant disease detection. Front Plant Sci, 2016, 7:1–10
93 Brahimi M, Boukhalfa K, Moussaoui A. Deep learning for tomato diseases:Classification and symptoms visualization. Appl Artif Intel, 2017,31 :299–315
94 Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. In:Mitschang B, Nicklas D, eds. The17 th Conference on Database Systems for Business, Technology, and Web. Stuttgart:Gesellschaft für Informatik e.V., 2017. 79–88
95 Oppenheim D, Shani G. Potato disease classification using convolution neural networks. Adv Anim Biosci, 2017, 8:244–249
96 Too E C, Yujian L, Njuki S, et al. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric, 2018, doi:10.1016/j.compag.2018.03.032
97 Liu B, Zhang Y, He D J, et al. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 2018, 10:11
98 Kawasaki Y, Uga H, Kagiwada S, et al. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In:Bebis G, Boyle R, eds. Advances in Visual Computing. Cham:Springer International Publishing, 2015. 638–645
99 Fujita E, Kawasaki Y, Uga H, et al. Basic investigation on a robust and practical plant diagnostic system. In:15th IEEE International Conference on Machine Learning and Applications(ICMLA). New York:IEEE, 2016. 989–992
100 Fuentes A, Yoon S, Kim S C, et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors,2017, 17:2022–2042
101 Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci, 2017, 2017:1–8
102 Ramcharan A, McCloskey P, Baranowski K, et al. Assessing a mobile-based deep learning model for plant disease surveillance. Comput Sci,2018, 1–13
103 Picon A, Alvarez-Gila A, Seitz M, et al. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agri, 2018, doi:10.1016/j.compag.2018.04.002
104 Johannes A, Picon A, Alvarez-Gila A, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case.Comput Electron Agri, 2017, 138:200–209
105 Li Y, Cui Z, Ni Y, et al. Plant density effect on grain number and weight of two winter wheat cultivars at different spikelet and grain positions.PLoS ONE, 2016, 11:e0155351
106 Pound MP, Atkinson J A, Wells D M, et al. Deep learning for multi-task plant phenotyping. In:16th IEEE International Conference on Computer Vision(ICCV)Venice. New York:IEEE, 2017. 2055–2063
107 Madec S, Jin X, Lu H, et al. Ear density estimation from high resolution rgb imagery using deep learning technique. Agric For Meteorol, 2019,264 :225–234
108 Lu H, Cao Z, Xiao Y, et al. Tasselnet:Counting maize tassels in the wild via local counts regression network. Plant Method, 2017, 13:79–92
109 Kuwata K, Shibasaki R. Estimating crop yields with deep learning and remotely sensed data. In:IEEE International Geoscience and Remote Sensing Symposium(IGARSS). New York:IEEE, 2015. 858–861
110 You J, Li X, Low M, et al. Deep gaussian process for crop yield prediction based on remote sensing data. In:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2017. 4559–4566
111 Chen S W, Shivakumar S S, Dcunha S, et al. Counting apples and oranges with deep learning:A data-driven approach. IEEE Robot Autom Lett,2017, 2:781–788
112 Pothen Z S, Nuske S. Texture-based fruit detection via images using the smooth patterns on the fruit. In:Okamura A, Menciassi A, eds. 2016IEEE International Conference on Robotics and Automation(ICRA). New York:IEEE, 2016. 5171–5176
113 Rahnemoonfar M, Sheppard C. Deep count:Fruit counting based on deep simulated learning. Sensors, 2017, 17:905–916
114 Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning. In:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, AAAI, 2017. 12–18
115 Bargoti S, Underwood J P. Image segmentation for fruit detection and yield estimation in apple orchards. J Field Robotics, 2017, 34:1039–1060
116 Hung C, Underwood J, Nieto J, et al. A feature learning based approach for automated fruit yield estimation. In:Mejias L, Corke P, eds. The 9th Conference on Field and Service Robotics. Cham:Springer International Publishing, 2015. 485–498
117 Roerdink J B, Meijster A. The watershed transform:Definitions, algorithms and parallelization strategies. Fund Inform, 2000, 41:187–228
118 Atherton T J, Kerbyson D J. Size invariant circle detection. Image Vision Comput, 1999, 17:795–803