Automatic rice crop height measurement using a field server and digital image processing

Sensors (Switzerland)

Volumn 14, Issue 1, 2014, Pages 900-926

  Sritarapipat, Tanakorn ^a   Rakwatin, Preesan ^a   Kasetkasem, Teerasit ^b  

^a GEO INFORMATICS AND SPACE TECHNOLOGY DEVELOPMENT AGENCY   (Thailand)

^b KASETSART UNIVERSITY   (Thailand)

Author keywords

Digital image processing; Field server; Image segmentation; Rice crop height measurement

Indexed keywords

DIGITAL CAMERAS; IMAGE PROCESSING; IMAGE SEGMENTATION; VIDEO CAMERAS;

AGRONOMIC TRAITS; AUTOMATIC IMAGE PROCESSING; FIELD SERVER; HEIGHT MEASUREMENT; HUMAN INTERVENTION; REDUNDANT FEATURES; RICE CROPS; THRESHOLDING METHODS;

CROPS;

EID: 84891879616     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140100900     Document Type: Article

References (44)

1. Woebbecke, D.M.; Meyer, G.E.; von Bargen, K.; Mortensen, D.A. Shape features for identifying young weeds using image analysis. Trans. ASAE 1995, 38, 271-281.
2. Lukina, E.V.; Stone, M.L.; Raun, W.R. Estimating vegetation coverage in wheat using digital images. J. Plant Nutr. 1999, 22, 341-350.
3. Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine Vision Detection Parameters for Plant Species Identification. In Proceedings of the SPIE 3543, Precision Agriculture and Biological Quality, Boston, MA, USA, 14 January 1999; pp. 327-335.
4. Perez, A.J.; Lopez, F.; Benlloch, J.V.; Christensen, S. Colour and shape analysis techniques for weed detection in cereal fields. J. Comput. Electron. Agric. 2000, 25, 197-212.
5. Demarez, V.; Duthoit, S.; Baret, F.; Weiss, M.; Dedieu, G. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. J. Agric. For. Meteorol. 2008, 148, 644-655.
6. Sakamoto, T.; Wardlow, B.D.; Gitelson, A.A. Detecting spatiotemporal changes of corn developmental stages in the US corn belt using MODIS WDRVI data. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1926-1936.
7. Shibayama, M.; Sakamoto, T.; Takada, E.; Inoue, A.; Morita, K.; Takahashi, W.; Kimura, A. Estimating paddy rice leaf area index with fixed point continuous observation of near infrared reflectance using a calibrated digital camera. J. Plant Prod. Sci. 2011, 14, 30-46.
8. Fukatsu, T.; Hirafuji, M. Field monitoring using sensor-nodes with a Web server. J. Robot. Mechatron. 2005, 17, 164-172.
9. Kahn, J.M.; Katz, R.H.; Pister, K.S.J. Next Century Challenges: Mobile Networking for Smart Dust. In Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, WA, USA, 15-19 August 1999; pp. 271-278.
10. Delin, K.A.; Jackson, S.P. Sensor Web for in Situ Exploration of Gaseous Biosignatures. In Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, 18-25 March 2000; pp. 465-472.
11. Fukatsu, T.; Hirafuji, M.; Kiura, T. An agent system for operating web-based sensor nodes via the internet. J. Robot. Mechatron. 2006, 18, 186-194.
12. Fukatsu, T.; Saito, Y.; Suzuki, T.; Kobayashi, K.; Hirafuji, H. A Long-Term Field Monitoring System with Field Servers at a Grape Farm. In Proceedings of International Symposium on Application of Precision Agriculture for Fruits and Vegetables, Orlando, FL, USA, 6-9 January 2008; pp. 183-190.
13. Wan, Y.; Yu, S.; Huang, J.; Yang, J.; Tsai, C. Automation Integration for Taiwan Country Chicken Farm Management Using Field Server. In Proceedings of the IAALD AFITA WCCA 2008, World Conference on Agricultural Information and IT, Tokyo, Japan, 24-27 August 2008.
14. Fukatsu, T.; Watanabe, T.; Hu, H.; Yoichi, H.; Hirafuji, M. Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants, Field Servers, and image analysis. J. Comput. Electron. Agric. 2012, 80, 8-16.
15. Venuprasad, R.; Sta Cruz, M.T.; Amante, M.; Magbanua, R.; Kumar, A.; Atlin, G.N. Response to two cycles of divergent selection for grain yield under drought stress in four rice breeding populations. J. Field Crops Res. 2008, 107, 232-244.
16. 2 on the growth, seed yield, and water use efficiency of soybean (Glycine max (L.) Merr.) under drought stress. Agric. Water Manag. 2013, 129, 105-112.
17. Qing, Z.; Yongci, L.; Shouzheng, T. Tree height measurement based on affine reconstructure. Int. J. Comput. Eng. Appl. 2005, 31, 21-22.
18. Changgui, Z.; Wenyi, F. Error effect of exterior orientation elements on tree height measurement using space intersection theory. J. Northeast For. Univ. 2007, 35, 86-91.
19. Zhang, J.; Huang, X. Measuring Method of Tree Height Based on Digital Image Processing Technology. In Proceedings of the First International Conference on Information Science and Engineering, Nanjing, China, 26-28 December 2009; pp. 1327-1331.
20. Han, D. Tree Height Measurement Based on Image Processing with 3-Points Correction. In Proceedings of the International Conference on Computer Science and Network Technology, Harbin, China, 24-26 December 2011; pp. 2281-2284.
21. Zhang, L.; Grift, T.E. A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput. Electron. Agric. 2012, 85, 70-76.
22. Ulander, L.M.H.; Dammert, P.B.G.; Hagberg, J.O. Measuring Tree Height Using ERS-1 SAR Interferometry. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Firenze, Italy, 10-14 July 1995; pp. 2189-2193.
23. Dammert, P.B.G.; Askne, J. Interferometric Tree Height Observations in Boreal Forests with SAR Interferometry. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Seattle, WA, USA, 6-10 July 1998; pp. 1363-1366.
24. Shimada, M.; Muhtar, Q.; Tadono, T.; Wakabayashi, H. Tree Height Estimation Using an Airborne L-Band Polarimetric Interferometric SAR. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia, 9-13 July 2001; pp. 1430-1432.
25. Nilsson, M. Estimation of tree heights and stand volume using an airborne LIDAR system. J. Remote Sens. Environ. 1996, 56, 1-7.
26. Magnussen, S.; Eggermonth, P.; Riccia, V.L. Recovering tree heights from airborne LIDAR data. J. For. Sci. 1999, 45, 407-422.
27. Persson, Å.; Holmgren, J.; Söderman, U. Detecting and measuring individual trees using an airborne LIDAR. J. Photogramm. Eng. Remote Sens. 2002, 68, 925-932.
28. St-Onge, B.; Hu, Y.; Véga, C. Mapping the height and above-ground biomass of a mixed forest using LIDAR and stereo Ikonos images. Int. J. Remote Sens. 2007, 29, 1277-1294.
29. Kwak, D.; Lee, W.; Lee, J.; Biging, G.S.; Gong, P. Detection of individual trees and estimation of tree height using LiDAR data. J. For. Res. 2007, 12, 425-434.
30. Llorens, J.; Gil, E.; Llop, J.; Escolà, A. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards. Advances to improve pesticide application methods. J. Sens. 2010, 11, 2177-2194.
31. Yamamoto, K.; Takahashi, T.; Miyachi, Y.; Kondo, N.; Morita, S.; Nakao, M.; Takashi, S.; Shibayama, T.; Takaichi, Y.; Tsuzuku, M.; et al. Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model. J. For. Res. 2011, 16, 425-431.
32. Chang, T.; Bardenas, A.B. The Morphology and Varietal Characteristics of the Rice Plant; Technical Bullet 4; the International Rice Research Institute: Los Baños, Philippines, December 1965.
33. Stevens, G.; Wrather, A.; Rhine, M.; Vories, E.; Dunn, D. Predicting rice yield response to midseason Nitrogen with plant area measurements. J. Am. Soc. Agron. 2008, 100, 387-392.
34. Ngouajioa, M.; Lerouxa, G.D.; Lemieux, C. Influence of images recording height and crop growth stage on leaf cover estimates and their performance in yield prediction models. J. Crop Prot. 1999, 18, 501-508.
35. Harrella, D.L.; Bondb, J.A.; Blanchea, S. Evaluation of main-crop stubble height on ratoon rice growth and development. J. Field Crops Res. 2009, 114, 396-403.
36. Boomsma, C.R.; Santini, J.B.; West, T.D.; Brewer, J.C.; McIntyre, L.M.; Vyn, T.J. Maize grain yield responses to plant height variability resulting from crop rotation and tillage system in a long-term experiment. J. Soil Tillage Res. 2010, 106, 227-240.
37. Takaya, Y. The plant height of rice in delta. J. Southeast Asian Stud. 1973, 11, 267-276.
38. Gonzales, R.; Woods, R. Digital Image Processing, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2008.
39. Mausel, P.W.; Kramber, W.J.; Lee, J.K. Optimal band selection for supervised classification of multispectral data. J. Photogramm. Eng. Remote Sens. 1990, 56, 55-60.
40. Meyer, G.E.; Mehta, T.; Kocher, M.F.; Mortensen, D.A.; Samal, A. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE 1998, 41, 1189-1197.
41. Lindeberg, T. Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 1998, 30, 117-154.
42. Reuter, M.; Biasotti, S.; Giorgi, D.; Patane, G.; Spagnuolo, M. Discrete Laplace-Beltrami operators for shape analysis and segmentation. J. Comput. Graph. 2009, 33, 381-390.
43. Sezgin, M.; Sankur, B. Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 2004, 13, 146-165.
44. Bickel, P.J.; Doksum, K.A. Mathematical Statistics; Prentice Hall: Eaglewood cliffs, NJ, USA, 1977.