In-field automatic observation of wheat heading stage using computer vision

Biosystems Engineering

Volumn 143, Issue , 2016, Pages 28-41

  Zhu, Yanjun ^a   Cao, Zhiguo ^a   Lu, Hao ^a   Li, Yanan ^a   Xiao, Yang ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Automatic observation; Computer vision; FV; Heading stage; SIFT

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; ARTIFICIAL INTELLIGENCE; IMAGE PROCESSING; LEARNING SYSTEMS; STATISTICAL TESTS;

AGRICULTURAL MANAGEMENT; AUTOMATIC OBSERVATION; COMPUTER VISION TECHNOLOGY; FV; MACHINE LEARNING TECHNOLOGY; MID-LEVEL REPRESENTATION; SCALE INVARIANT FEATURE TRANSFORMS; SIFT;

COMPUTER VISION;

EID: 84954510664     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2015.12.015     Document Type: Article

References (54)

1. Administration C.M. Specifications for agrometeorological observation 1993, Vol. 1. China Meteorological Press, Beijing.
2. Angus J., Mackenzie D., Morton R., Schafer C. Phasic development in field crops ii. Thermal and photoperiodic responses of spring wheat. Field Crops Research 1981, 4:269-283.
3. Bannayan M., Sanjani S. Weather conditions associated with irrigated crops in an arid and semi arid environment. Agricultural and Forest Meteorology 2011, 151:1589-1598.
4. Bosch A., Zisserman A., Muñoz X. Scene classification via plsa. Proc. European Conference on Computer Vision (ECCV) 2006, 517-530. Springer.
5. Bosch A., Zisserman A., Muoz X. Image classification using random forests and ferns. Proc. IEEE International Conference on Computer Vision (ICCV) 2007, 1-8. IEEE.
6. Campbell N.A. The decorrelation stretch transformation. International Journal of Remote Sensing 1996, 17:1939-1949.
7. Chang C.-C., Lin C.-J. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2011, 2(27):1-27.
8. Cheng Y., Hu X., Zhang C. Algorithm for segmentation of insect pest images from wheat leaves based on machine vision. Transactions of the Chinese Society of Agricultural Engineering 2007, 2007.
9. Cimpoi M., Maji S., Kokkinos I., Mohamed S., Vedaldi A. Describing textures in the wild. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014, 3606-3613. IEEE.
10. Cinbis R.G., Verbeek J., Schmid C. Approximate fisher kernels of non-iid image models for image categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 2015, 1-14.
11. Cointault F., Guérin D., Guillemin J.-P., Chopinet B. In-field triticum aestivum ear counting using colour-texture image analysis. New Zealand Journal of Crop and Horticultural Science 2008, 36:117-130.
12. Cointault F., Journaux L., Rabatel G., Germain C., Ooms D., Destain M.-F., et al. Texture, color and frequential proxy-detection image processing for crop characterization in a context of precision agriculture. Agricultural Science 2012, 49-70.
13. Cook R.J., Veseth R.J. Wheat health management 1991, APS Press, St. Paul, MN.
14. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
15. Fan R.-E., Chang K.-W., Hsieh C.-J., Wang X.-R., Lin C.-J. Liblinear: a library for large linear classification. Journal of Machine Learning Research 2008, 9:1871-1874.
16. Fang Y., Chang T., Zhai R., Wang X. Automatic recognition of rape seeding emergence stage based on computer vision technology. Proc. IEEE International Conference on Agro-geoinformatics 2014, 1-5. IEEE.
17. Gong A., Yu J., He Y., Qiu Z. Citrus yield estimation based on images processed by an android mobile phone. Biosystems Engineering 2013, 115:162-170.
18. Guerrero J.M., Pajares G., Montalvo M., Romeo J., Guijarro M. Support vector machines for crop/weeds identification in maize fields. Expert Systems with Applications 2012, 39:11149-11155.
19. Hsu C.-W., Chang C.-C., Lin C.-J. A practical guide to support vector classification 2003.
20. Jaakkola T., Haussler D. Exploiting generative models in discriminative classifiers. Proc. Advances in Neural Information Processing Systems (NIPS) 1999, 487-493.
21. Jannoura R., Brinkmann K., Uteau D., Bruns C., Joergensen R.G. Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems Engineering 2015, 129:341-351.
22. Jiang H., Wang J., Yuan Z., Wu Y., Zheng N., Li S. Salient object detection: a discriminative regional feature integration approach. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013, 2083-2090. IEEE.
23. Kurtulmuş F., Kavdir I. Detecting corn tassels using computer vision and support vector machines. Expert Systems with Applications 2014, 41:7390-7397.
24. Lei B., Yao Y., Chen S., Li S., Li W., Ni D., et al. Discriminative learning for automatic staging of placental maturity via multi-layer fisher vector. Scientific Reports 2015, 5.
25. Li L.-J., Li F.-F. What, where and who? Classifying events by scene and object recognition. Proc. IEEE International Conference on Computer Vision (ICCV) 2007, 1-8. IEEE.
26. Lin T.-Y., RoyChowdhury A., Maji S. Bilinear CNN models for fine-grained visual recognition 2015, arXiv preprint arXiv:1504.07889.
27. Liu T., Sun C., Wang L., Zhong X., Zhu X., Guo W. In-field wheatear counting based on image processing technology. Transactions of the Chinese Society for Agricultural Machinery 2014, 45:282-290.
28. Lowe D.G. Object recognition from local scale-invariant features. Proc. IEEE International Conference on Computer Vision (ICCV) 1999, 1150-1157. IEEE.
29. Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004, 60:91-110.
30. Payne A.B., Walsh K.B., Subedi P., Jarvis D. Estimation of mango crop yield using image analysis - segmentation method. Computers and Electronics in Agriculture 2013, 91:57-64.
31. Pedersoli M., Vedaldi A., Gonzalez J., Roca X. A coarse-to-fine approach for fast deformable object detection. Pattern Recognition 2015, 48:1844-1853.
32. Perronnin F., Sánchez J., Mensink T. Improving the fisher kernel for large-scale image classification. Proc. European Conference on Computer Vision (ECCV) 2010, 143-156. Springer.
33. Polder G., van der Heijden G.W., van Doorn J., Baltissen T.A. Automatic detection of tulip breaking virus (tbv) in tulip fields using machine vision. Biosystems Engineering 2014, 117:35-42.
34. Pourreza A., Lee W.S., Etxeberria E., Banerjee A. An evaluation of a vision-based sensor performance in huanglongbing disease identification. Biosystems Engineering 2015, 130:13-22.
35. Richards J.A. Remote sensing digital image analysis 2013, Springer. 5th ed.
36. Riche N., Mancas M., Gosselin B., Dutoit T. Rare: a new bottom-up saliency model. Proc. IEEE International Conference on Image Processing (ICIP) 2012, 641-644. IEEE.
37. Sakamoto T., Gitelson A.A., Nguy-Robertson A.L., Arkebauer T.J., Wardlow B.D., Suyker A.E., et al. An alternative method using digital cameras for continuous monitoring of crop status. Agricultural and Forest Meteorology 2012, 154:113-126.
38. Sánchez J., Perronnin F., Mensink T., Verbeek J. Image classification with the fisher vector: theory and practice. International Journal of Computer Vision 2013, 105:222-245.
39. Simonyan K., Vedaldi A., Zisserman A. Deep fisher networks for large-scale image classification. Proc. Advances in Neural Information Processing Systems (NIPS) 2013, 163-171.
40. Soha J.M., Schwartz A.A. Multispectral histogram normalization contrast enhancement. Proc. Canadian Symposium on Remote Sensing 1978, Vol. 1:86-93.
41. Sun C., Berman M., Coward D., Osborne B. Thickness measurement and crease detection of wheat grains using stereo vision. Pattern Recognition Letters 2007, 28:1501-1508.
42. Taylor M.M. Principal components colour display of erts imagery 1974.
43. Tellaeche A., Burgos-Artizzu X.P., Pajares G., Ribeiro A. A vision-based method for weeds identification through the bayesian decision theory. Pattern Recognition 2008, 41:521-530.
44. Tellaeche A., Pajares G., Burgos-Artizzu X.P., Ribeiro A. A computer vision approach for weeds identification through support vector machines. Applied Soft Computing 2011, 11:908-915.
45. Valiente-González J.M., Andreu-García G., Potter P., Rodas-Jordá Á. Automatic corn (zea mays) kernel inspection system using novelty detection based on principal component analysis. Biosystems Engineering 2014, 117:94-103.
46. Vega F.A., Ramírez F.C., Saiz M.P., Rosúa F.O. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering 2015, 132:19-27.
47. Vinyals O., Jia Y., Deng L., Darrell T. Learning with recursive perceptual representations. Proc. Advances in Neural Information Processing Systems (NIPS) 2012, 2825-2833.
48. Wang Y., Cao Z., Bai X., Yu Z., Li Y. An automatic detection method to the field wheat based on image processing. Proc. International Symposium on Multispectral Image Processing and Pattern Recognition 2013, 89180F. International Society for Optics and Photonics. 10.1117/12.2031139.
49. Ye M., Cao Z., Yu Z. An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency. Proc. International Symposium on Multispectral Image Processing and Pattern Recognition 2013, 89210Z. International Society for Optics and Photonics. 10.1117/12.2031024.
50. Yeh Y.-H.F., Lai T.-C., Liu T.-Y., Liu C.-C., Chung W.-C., Lin T.-T. An automated growth measurement system for leafy vegetables. Biosystems Engineering 2014, 117:43-50.
51. You J., Bhattacharya P. A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment. IEEE Transactions on Image Processing 2000, 9:1547-1559.
52. Yu Z., Cao Z., Wu X., Bai X., Qin Y., Zhuo W., et al. Automatic image-based detection technology for two critical growth stages of maize: emergence and three-leaf stage. Agricultural and Forest Meteorology 2013, 174:65-84.
53. Zayas I., Flinn P. Detection of insects in bulk wheat samples with machine vision. Transactions of the ASAE-American Society of Agricultural Engineers 1998, 41:883-888.
54. Zhang N., Chaisattapagon C. Effective criteria for weed identification in wheat fields using machine vision. Transactions of the ASAE 1995, 38:965-974.