Detection of corn and weed species by the combination of spectral, shape and textural features

Sustainability (Switzerland)

Volumn 9, Issue 8, 2017, Pages

  Lin, Fenfang ^a   Zhang, Dongyan ^b   Huang, Yanbo ^c   Wang, Xiu ^d   Chen, Xinfu ^e  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b ANHUI UNIVERSITY   (China)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

^d BEIJING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^e Fujian Transportation Vocational College   (China)

Author keywords

Corn; Decision tree; Hyper spectral imaging; Object oriented; Weed

Indexed keywords

ACCESSIBILITY; ACCURACY ASSESSMENT; AGRICULTURAL LAND; ALGORITHM; DECISION MAKING; DETECTION METHOD; DISCRIMINANT ANALYSIS; ENTROPY; MAIZE; NDVI; SHAPE; SPECTRAL ANALYSIS; SPECTROMETER; TEXTURE; WEED;

ZEA MAYS;

EID: 85026824347     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su9081335     Document Type: Article

References (31)

1. Qiang, S. Weed Science, 2nd ed.; China Agriculture Press: Beijing, China, 2009.
2. Cope, J.S.; Corney, D.; Clark, J.Y.; Remagnino, P.; Wilkin, P. Plant species identification using digital morphometrics: A review. Expert Syst. Appl. 2012, 39, 7562–7573. [CrossRef]
3. Granitto, P.M.; Navone, H.D.; Verdes, P.F.; Ceccatto, H.A. Weed seeds identification by machine vision. Comput. Electron. Agric. 2002, 33, 91–103. [CrossRef]
4. Taghadomi-Saberi, S.; Hemmat, A. Improving field management by machine vision—A review. Agric. Eng. Int. CIGR J. 2015, 17, 92–111.
5. Borregaard, T.; Nielsen, H.; Nørgaard, L.; Have, H. Crop-weed discrimination by line imaging spectroscopy. J. Agric. Eng. Res. 2000, 75, 389–400. [CrossRef]
6. Guerrero, J.M.; Pajares, G.; Montalvo, M.; Romeo, J.; Guijarro, M. Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl. 2012, 39, 11149–11155. [CrossRef]
7. Lamb, D.W.; Brown, R.B. Remote-sensing and mapping of weeds in crops. J. Agric. Eng. Res. 2001, 78, 117–125. [CrossRef]
8. Meyer, G.E. Machine vision detection parameters for plant species identification. Proc. SPIE 1999, 3543. [CrossRef]
9. Mao, W.; Hu, X.; Zhang, X. Weed detection based on the optimized segmentation line of crop and weed. In Computer and Computing Technologies in Agriculture, Volume ii, In Proceedings of the First ifip tc 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, 18–20 August 2007; Li, D., Ed.; Springer: Boston, MA, USA, 2008; pp. 959–967.
10. Meyer, G.E. Design of an optical weed sensor using plant spectral characteristics. Trans. ASAE 2001, 44, 409–419.
11. Moshou, D.; Kateris, D.; Pantazi, X.E.; Gravalos, I. Crop and weed species recognition based on hyperspectral sensing and active learning. In Precision Agriculture '13; Stafford, J.V., Ed.; Wageningen Academic Publishers: Wageningen, The Netherlands, 2013; pp. 555–561.
12. Piron, A.; Leemans, V.; Kleynen, O.; Lebeau, F.; Destain, M.F. Selection of the most efficient wavelength bands for discriminating weeds from crop. Comput. Electron. Agric. 2008, 62, 141–148. [CrossRef]
13. Symonds, P.; Paap, A.; Alameh, K.; Rowe, J.; Miller, C. A real-time plant discrimination system utilising discrete reflectance spectroscopy. Comput. Electron. Agric. 2015, 117, 57–69. [CrossRef]
14. Li, Y.; Zhang, L.; Wei, Y.; Huang, C. Weed identification using imaging spectrometer data. J. Remote Sens. 2013, 78, 87–89.
15. Tits, L.; Keersmaecker, W.D.; Somers, B.; Asner, G.P.; Farifteh, J.; Coppin, P. Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring. ISPRS J. Photogramm. Remote Sens. 2012, 74, 163–174. [CrossRef]
16. Somers, B.; Delalieux, S.; Verstraeten, W.W.; Verbesselt, J.; Lhermitte, S.; Coppin, P.; Entekhabi, D.; Kerekes, J.P.; Miller, E.L.; Reising, S.C. Magnitude- and shape-related feature integration in hyperspectral mixture analysis to monitor weeds in citrus orchards. Geosci. Remote Sens. IEEE Trans. 2009, 47, 3630–3642. [CrossRef]
17. Alchanatis, V.; Ridel, L.; Hetzroni, A.; Yaroslavsky, L. Weed detection in multi-spectral images of cotton fields. Comput. Electron. Agric. 2005, 47, 243–260. [CrossRef]
18. Tong, Q.; Xue, Y.; Wang, J.; Zhang, L.; Fang, J.; Yang, Y.; Liu, X.; Hongxing, Q.I.; Zheng, L. Development and application of the field imaging spectrometer system. J. Remote Sens. 2010, 14, 409–422.
19. Jia, X.; Richards, J.A. Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Trans. Geosci. Remote Sens. 1999, 37, 538–542.
20. Pearson, R.L.; Miller, L.D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In Proceedings of the Eighth International Symposium on Remote Sensing of Environment, Environmental Research Institute of Michigan, Ann Arbor, MI, USA, 2–6 October 1972; pp. 1357–1381.
21. Escadafal, R.; Huete, A. Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil “noise”. C. R. Acad. Sci. 1991, 312, 1385–1391.
22. Maccioni, A.; Agati, G.; Mazzinghi, P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J. Photochem. Photobiol. B Biol. 2001, 61, 52. [CrossRef]
23. Gitelson, A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B Biol. 1994, 22, 247–252. [CrossRef]
24. GmbH, T.G. Ecognition Developer 8.7: Reference Book; Trimble: Munich, Germany, 2011.
25. Haralick, R.M. Statistical and structural approaches to texture. Proc. IEEE 1979, 67, 786–804. [CrossRef]
26. Quinlan, J.R. C 4.5:Programs for Machine Learning; Morgan Kaufmann: San Francisco, CA, USA, 1992.
27. Patil, N.; Lathi, R.; Chitre, V. Comparison of C 5.0 & Cart Classification Algorithms Using Pruning Technique. Int. J. Eng. Res. Technol. 2012, 1, 1–5.
28. Boulle, M. Khiops: A statistical discretization method of continuous attributes. Mach. Learn. 2004, 55, 53–69. [CrossRef]
29. Liu, B.; Fang, J.Y.; Liu, X.; Zhang, L.F.; Zhang, B.; Tong, Q.X. Research on crop-weed discrimination using a field imaging spectrometer. Spectrosc. Spectr. Anal. 2010, 30, 1830–1833.
30. Zhang, D.; Qin, Z.; Wei, D.; Xiu, W. Using hyperspectral sensor to recognize monocotyledonous/dicotyledonous weeds and crops. Infrared Laser Eng. 2013, 42, 208–213.
31. Zwiggelaar, R. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot. 1998, 17, 189–206. [CrossRef]