Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations

Annals of Botany

Volumn 121, Issue 5, 2018, Pages 1079-1088

  Hui, Fang ^a   Zhu, Jinyu ^b   Hu, Pengcheng ^a   Meng, Lei ^c   Zhu, Binglin ^a   Guo, Yan ^a   Li, Baoguo ^a   Ma, Yuntao ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b INSTITUTE OF PLANT PROTECTION   (China)

^c WESTERN MICHIGAN UNIVERSITY   (United States)

Author keywords

3D evaluation; canopy structure; Image based; laser scanning; multi view stereo; plant population

Indexed keywords

ACCURACY ASSESSMENT; BREEDING; CANOPY ARCHITECTURE; FRUITING; IMAGE ANALYSIS; PHENOTYPE; QUANTITATIVE ANALYSIS; SEEDLING; STEREO IMAGE; THREE-DIMENSIONAL MODELING;

CUCUMIS SATIVUS; SOLANUM MELONGENA;

AGRICULTURE; ANATOMY AND HISTOLOGY; AUBERGINE; CUCUMBER; EVALUATION STUDY; FRUIT; GROWTH, DEVELOPMENT AND AGING; IMAGE PROCESSING; PHENOTYPE; PIPER (PLANT); PLANT BREEDING; PLANT LEAF; PROCEDURES; SEEDLING;

AGRICULTURE; CUCUMIS SATIVUS; FRUIT; IMAGE PROCESSING, COMPUTER-ASSISTED; PHENOTYPE; PIPER; PLANT BREEDING; PLANT LEAVES; SEEDLINGS; SOLANUM MELONGENA;

EID: 85046264131     PISSN: 03057364     EISSN: 10958290     Source Type: Journal    
DOI: 10.1093/aob/mcy016     Document Type: Article

References (59)

1. Ai XZ, Zhang ZX, He QW, Sun XL, Xing YX. 2002. Study on photosynthesis of leaves at different positions of cucumber in solar greenhouse. Scientia Agricultura Sinica 35: 1519-1524.
2. Arya S, Mount DM, Netanyahu NS, Silverman R, Wu AY. 1998. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM 45: 891-923.
3. Aspert N, Santa-Cruz D, Ebrahimi T. 2002. MESH: measuring errors between surfaces using the Hausdorff distance. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) 2002. Piscataway: IEEE, 705-708.
4. Besl PJ, Jain RC. 1988. Segmentation through variable-order surface fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence 10: 167-192.
5. Biskup B, Scharr H, Schurr U, Rascher UWE. 2007. A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell & Environment 30: 1299-1308.
6. Blum A. 2005. Drought resistance, water-use efficiency, and yield potential-are they compatible, dissonant, or mutually exclusive Crop and Pasture Science 56: 1159-1168.
7. Burgess AJ, Retkute R, Pound MP, Mayes S, Murchie EH. 2017. Imagebased 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems. Annals of Botany 119: 517-532.
8. Cignoni P, Rocchini C, Scopigno R. 1998. Metro: measuring error on simplified surfaces. Computer Graphics Forum 17: 167-174.
9. Critchley C. 1981. Studies on the mechanism of photoinhibition in higher plants I. Effects of high light intensity on chloroplast activities in cucumber adapted to low light. Plant Physiology 67: 1161-1165.
10. Dey D, Mummert L, Sukthankar R. 2012. Classification of plant structures from uncalibrated image sequences. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV) 2012. Piscataway: IEEE, 329-336.
11. Dornbusch T, Wernecke P, Diepenbrock W. 2007. A method to extract morphological traits of plant organs from 3D point clouds as a database for an architectural plant model. Ecological Modelling 200: 119-129.
12. Duan T, Chapman SC, Holland E, Rebetzke GJ, Guo Y, Zheng B. 2016. Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes. Journal of Experimental Botany 67: 4523-4534.
13. Furbank RT, Tester M. 2011. Phenomics-technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16: 635-644.
14. Furukawa Y, Ponce J. 2010. Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence 32: 1362-1376.
15. Ghandi A, Adi M, Lilia F, et al. 2016. Tomato yellow leaf curl virus infection mitigates the heat stress response of plants grown at high temperatures. Scientific Reports 6: 19715.
16. Guo Y, Shi TX, Wu J, Li X, Xu ZL, Yang YH. 2012. The development of static virtual tobacco model basing on three dimensional scanning methodology. Acta Tabacaria Sinica 18: 29-33.
17. Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F. 2011. HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12: 148.
18. Hosoi F, Omasa K. 2006. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Transactions on Geoscience and Remote Sensing 44: 3610-3618.
19. Hou T, Zheng B, Xu Z, Yang Y, Chen Y, Guo Y. 2016. Simplification of leaf surfaces from scanned data: effects of two algorithms on leaf morphology. Computers and Electronics in Agriculture 121: 393-403.
20. Houle D, Govindaraju DR, Omholt S. 2010. Phenomics: The next challenge. Nature Reviews Genetics 11: 855-866.
21. Hsiao TC, O'Toole JC, Yambao EB, Turner NC. 1984. Influence of osmotic adjustment on leaf rolling and tissue death in rice (Oryza sativa L.). Plant Physiology 75: 338-341.
22. Hu P, Guo Y, Li B, Zhu J, Ma Y. 2015. Three-dimensional reconstruction and its precision evaluation of plant architecture based on multiple view stereo method. Transactions of the Chinese Society of Agricultural Engineering 31: 209-214.
23. Ihaka R, Gentleman R. 1996. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5: 299-314.
24. Jin J, Tang L. 2009. Corn plant sensing using real-time stereo vision. Journal of Field Robotics 26: 591-608.
25. Kadioglu A, Terzi R, Saruhan N, Saglam A. 2012. Current advances in the investigation of leaf rolling caused by biotic and abiotic stress factors. Plant Science 182: 42-48.
26. Kazhdan M, Hoppe H. 2013. Screened Poisson surface reconstruction. ACM Transactions on Graphics 32: 29.
27. Kazmi W, Foix S, Alenyà G, Andersen HJ. 2014. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison. ISPRS Journal of Photogrammetry and Remote Sensing 88: 128-146.
28. Khalifa I, Moussa M, Kamel M. 2003. Range image segmentation using local approximation of scan lines with application to CAD model acquisition. Machine Vision and Applications 13: 263-274.
29. Klodt M, Herzog K, Töpfer R, Cremers D. 2015. Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinformatics 16: 143.
30. Kolev K, Klodt M, Brox T, Cremers D. 2009. Continuous global optimization in multiview 3D reconstruction. International Journal of Computer Vision 84: 80-96.
31. Lati RN, Filin S, Eizenberg H. 2013. Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points. Precision Agriculture 14: 586-605.
32. Lewin R. 1986. Proposal to sequence the human genome stirs debate. Science 232: 1598-1601.
33. Ma L, Ma J, Shen Y. 2007. Pixel fusion based curvelets and wavelets denoise algorithm. Engineering Letters 14: 130-134.
34. McCarthy CL, Hancock NH, Raine SR. 2010. Applied machine vision of plants: A review with implications for field deployment in automated farming operations. Intelligent Service Robotics 3: 209-217.
35. Minervini M, Scharr H, Tsaftaris SA. 2015. Image analysis: The new bottleneck in plant phenotyping [applications corner]. IEEE Signal Processing Magazine 32: 126-131.
36. Munns R, Tester M. 2008. Mechanisms of salinity tolerance. Annual Review of Plant Biology 59: 651-681.
37. Natonek E. 1998. Fast range image segmentation for servicing robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, 1998. Piscataway: IEEE, 406-411.
38. Nguyen CV, Fripp J, Lovell DR, et al. 2016. 3D scanning system for automatic high-resolution plant phenotyping. In: Wee-Chung Liew A, Lovell B, Fookes C, Zhou J, Gao Y, Blumenstein M, Wang Z. eds. Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2016. Piscataway: IEEE, 1-8.
39. Nguyen TT, Slaughter DC, Townsley BT, Carriedo L, Maloof JN, Sinha N. 2016. In-field plant phenotyping using multi-view reconstruction: An investigation in eggplant. In: Proceedings of the 13th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL: International Society of Precision Agriculture.
40. Omasa K, Hosoi F, Konishi A. 2007. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany 58: 881-898.
41. Paproki A, Sirault X, Berry S, Furbank R, Fripp J. 2012. A novel mesh processing based technique for 3D plant analysis. BMC Plant Biology 12: 63.
42. Paulus S, Dupuis J, Mahlein A-K, Kuhlmann H. 2013. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinformatics 14: 238.
43. Paulus S, Dupuis J, Riedel S, Kuhlmann H. 2014. Automated analysis of barley organs using 3D laser scanning: An approach for high throughput phenotyping. Sensors 14: 12670-12686.
44. Pound MP, French AP, Murchie EH, Pridmore TP. 2014. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiology 166: 1688-1698.
45. Pound MP, French AP, Fozard JA, Murchie EH, Pridmore TP. 2016. A patch-based approach to 3D plant shoot phenotyping. Machine Vision and Applications 27: 767-779.
46. Rabbani T, Van Den Heuvel F, Vosselmann G. 2006. Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36: 248-253.
47. Rajendran K, Tester M, Roy SJ. 2009. Quantifying the three main components of salinity tolerance in cereals. Plant, Cell & Environment 32: 237-249.
48. Rauf S, Al-Khayri JM, Zaharieva M, Monneveux P, Khalil F. 2016. Breeding strategies to enhance drought tolerance in crops. In: Al-Khayri JM, Jain SM, Johnson DV. eds. Advances in plant breeding strategies: Agronomic, abiotic and biotic stress traits. Cham: Springer, 397-445.
49. Rose J, Paulus S, Kuhlmann H. 2015. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. Sensors 15: 9651-9665.
50. Rusu RB, Cousins S. 2011. 3D is here: Point Cloud Library (PCL). In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2011. Piscataway: IEEE, 1-4.
51. Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M. 2008. Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems 56: 927-941.
52. Sappa AD, Devy M. 2001. Fast range image segmentation by an edge detection strategy In: Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling 2001. Los Alamitos; Piscataway; Minato-ku: IEEE, 292-299.
53. Schnurbusch T, Hayes J, Sutton T. 2010. Boron toxicity tolerance in wheat and barley: Australian perspectives. Breeding Science 60: 297-304.
54. Shafiekhani A, Kadam S, Fritschi F, DeSouza G. 2017. Vinobot and Vinoculer: Two robotic platforms for high-throughput field phenotyping. Sensors 17: 214.
55. Sithole G, Vosselman G. 2003. Automatic structure detection in a point-cloud of an urban landscape In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 2003. Piscataway: IEEE, 67-71.
56. Wani MA, Arabnia HR. 2003. Parallel edge-region-based segmentation algorithm targeted at reconfigurable multiring network. Journal of Supercomputing 25: 43-62.
57. Wu C. 2011. VisualSFM: A visual structure from motion system. http: //www. cs. washington. edu/homes/ccwu/vsfm
58. Zehr BE, Dudley JW, Rufener GK. 1994. QTLs for degree of pollen-silk discordance, expression of disease lesion mimic, and leaf curl response to drought. Maize Genetics Cooperation Newsletter 68: 110-111.
59. Zhang YH, Tang L, Liu XJ, Liu LL, Cao WX, Zhu Y. 2017. Modeling curve dynamics and spatial geometry characteristics of rice leaves. Journal of Integrative Agriculture 16: 2177-2190.