Imaging techniques for chemical application on crops

Phytoparasitica

Volumn 25, Issue SUPPL., 1997, Pages

  Hetzroni, Amots ^a   Edan, Yael ^a,b   Alchanatis, Victor ^a  

^a VOLCANI CENTER   (Israel)

^b BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Automation; Chemical spray; Color; Imaging; Pesticide application; Precision farming

Indexed keywords

EID: 0030769669     PISSN: 03342123     EISSN: None     Source Type: Journal    
DOI: 10.1007/bf02980332     Document Type: Article

References (50)

1. Al-Abbas, A.H., Barr, R., Hall, J.D., Crane, F.L. and Baumgardner, M.F. (1974) Spectra of normal and nutrient-deficient maize leaves. Agron. J. 66:16-20.
2. Anderson, N.W. and Wendorf, K.A. (1993) Analyzing plant images in the HLS color space. ASAE paper 93-3593. St. Joseph, MI, USA.
3. Bowers, S.A. and Hanks, R.J. (1964) Reflectance of radiant energy from soils. Soil Sci. 100:130-138.
4. Brown, R.B., Anderson, G.W., Proud, B. and Steckler, J.P. (1990) Herbicide application control using GIS weed maps. ASAE paper 90-1061. St. Joseph, MI, USA.
5. Brown, R.B., Steckler, J.P. and Anderson, G.W. (1991) Remote sensing for identification of weeds in no-till corn. ASAE paper 91-1050. St. Joseph, MI, USA.
6. Burks, T.F., Shearer, S.A. and Gates, R.S. (1994) Neural network classification of plant canopy images from texture features. ASAE paper 94-3510. St. Joseph, MI, USA.
7. Carter, G.A. (1991) Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot. 78:916-924.
8. Carter, G.A. (1993) Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80:239-243.
9. Chaisattapagon, C. and Zhang, N. (1992) Identifying effective criteria for weed detection using machine vision. ASAE paper 92-3576. St. Joseph, MI, USA.
10. Craven, J.B.J. and Katz, L.J. (1991) Evaluation of photoelectronic sensors for robotic transplanting. ASAE paper 91-7030. St. Joseph, MI, USA.
11. Felton, W.L. and McCloy, K.R. (1992) Spot spraying. Microprocessor-controlled, weed-detecting technology helps save money and the environment. Agric. Eng. 73(6):9-12.
12. Frankel, H. (1987) Israel Patent No. 69872. Optical sensor activated sprayer.
13. Franz, E., Gebhardt, M.R. and Unklesbay, K.B. (1991) Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Trans. ASAE 34:673-681.
14. Franz, E., Gebhardt, M.R. and Unklesbay, K.B. (1991) The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. ASAE 34:682-687.
15. Franz, E., Gebhardt, M.R. and Unklesbay, K.B. (1995) Algorithms for extracting leaf boundary information from digital images of plant foliage. Trans. ASAE 38:625-633.
16. Gan-Mor, S. and Law, S.E. (1992) Frequency and phase-lag effects on transport of particulates by an AC electric field. IEEE Trans. Biomed. Eng. IA-28:317-323.
17. Guyer, D.E., Miles, G.E., Gaultney, L.D. and Schreiber, M.M. (1993) Application of machine vision to shape analysis in leaf and plant identification. Trans. ASAE 36:163-171.
18. Guyer, D.E., Miles, G.E. and Schreiber, M.M. (1984) Computer vision and image processing for plant identification. ASAE paper 84-1632. St. Joseph, MI, USA.
19. Guyer, D.E., Miles, G.E., Schreiber, M.M. and Vanderbilt, V.C. (1986) Machine vision and image processing for plant identification. Trans. ASAE 29:1500-1507.
20. Hahn, F. and Muir, A.Y. (1992) The feasibility of the use of optical reflectance methods for the detection of weed and crop plants. Departmental note 52.
21. Hahn, F. and Muir, A.Y. (1993) Discrimination of weeds in cabbage, leek, potato and turnip crops. Departmental note 62.
22. Hetzroni, A. (1994) Machine Vision Monitoring of Plant Nutrition. Ph.D. thesis, Purdue University, West Lafayette, IN, USA.
23. Hooper, A.W., Harries, G.O. and Ambler, B. (1976) A photoelectric sensor for distinguishing between plant material and soil. J. Agric. Eng. Res. 21:145-155.
24. Humphries, S. and Simonton, W. (1993) Identification of plant parts using color and geometric image data. Trans. ASAE 36:1493-1500.
25. Kole, J., Ling, P.P. and Giacomelli, G.A. (1995) Determining nutrient stress in lettuce plants with machine vision technology. ASAE paper 95-4539. St. Joseph, MI, USA.
26. Kopeika, N., Levant, Y. and Fromouci, B. (1978) Selective spraying device. Israel Patent No. 54068.
27. Lord, D., Desjardin, R.L., Dube, P.A. and Brach, E.J. (1985) Variation of crop canopy spectral reflectance measurements under changing sky conditions. Photogram. Eng. Remote Sensing 51:689-695.
28. McGuire, R.G. (1992) Reporting of objective color measurements. HortScience 27:1254-1255.
29. Menges, R.M., Nixon, P.R. and Richardson, A.J. (1985) Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci. 33:569-581.
30. Meyer, G.E., Troyer, W.W., Fitzgerald, J.B. and Paparozzi, E.T. (1992) Leaf nitrogen analysis of poinsettia (Euphorbia pulcherrima Will D.) using spectral properties in natural and controlled lighting. Appl. Eng. Agric. 8:715-722.
31. Ninomiya, S., Oide, M., Furuta, N. and Ohmori, H. (1995) Evaluation of leaf and kernel shape based on principal component scores of standardized elliptic Fourier coefficients. ASAE paper 95-3220. St. Joseph, MI, USA.
32. Nitsch, B.B., Von Bargen, K., Meyer, G.E. and Mortensen, D.A. (1991) Visible and near infrared plant, soil and crop residue reflectivity for weed sensor design. ASAE paper 91-3006. St. Joseph, MI, USA.
33. Porteous, R.L. and Muir, A.Y. (1987) Some measurements of the performance of an experimental quality grading machine for potatoes. Potato Res. 30:24.
34. Richards, J.A. (1986) Remote Sensing Digital Image Analysis. Springer-Verlag, Berlin, Germany.
35. Rockwell, A.D. and Ayers, P.D. (1994) Variable rate sprayer development and evaluation. Trans. ASAE 10:327-333.
36. Shearer, S.A. and Holmes, R.G. (1990) Plant identification using color co-occurrence matrices. Trans. ASAE 33:2037-2044.
37. Shearer, S.A. and Jones, P.T. (1990) Selective application of post emergence herbicides using photoelectrics. ASAE paper 90-1581. St. Joseph, MI, USA.
38. Shropshire, G.J. (1991) Multi-spectral video imaging for plant identification. ASAE paper 91-3507. St. Joseph, MI, USA.
39. Shropshire, G.J., Von Bargen, K. and Rundquist, D. (1988) Fourier and Hadamard transform for weed population estimates from video images. ASAE paper 88-3039. St. Joseph, MI, USA.
40. Silva, L.F. (1978) Radiation and instrumentation in remote sensing. in: Swain, P.H. and Davis, S.M. [Eds.] Remote Sensing: The Quantitative Approach. McGraw-Hill, New York, NY.
41. Singh, N., Casady, W.W. and Costelo, T.A. (1994) Computer vision based nitrogen management system for wheat. ASAE paper 94-3512. St. Joseph, MI, USA.
42. Swain, P.H., Vanderbilt, V.C. and Jobusch, C.D. (1982) A quantitative applications-oriented evaluation of thematic mapper design specifications. IEEE Trans. Geosci. Remote Sensing GE-20:370-377.
43. Takebe, M., Yoneyama, T., Inada, K. and Murakami, T. (1990) Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant Soil 122:295-297.
44. Tarbell, K.A. and Reid, J.F. (1991) Spatial and spectral characteristics of corn leaves collected using computer vision. Trans. ASAE 34:2256-2263.
45. Thomas, J.R. and Oerther, G.F. (1972) Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agron. J. 64:11-13.
46. Tian, L. and Slaughter, D.C. (1993) Computer vision identification of tomato seedlings in natural outdoor scenes. ASAE paper 93-3608. St. Joseph, MI, USA.
47. Walter-Shea, E.A., Norman, J.M., Blad, B.L. and Robinson, B.F. (1991) Leaf reflectance and transmittance in soybean and corn. Agron. J. 83:631-636.
48. Woebbecke, D.M., Meyer, G.E., Von Bargen, K. and Mortensen, D.A. (1995) Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38:259-269.
49. Woebbecke, D.M., Meyer, G.E., Von Bargen, K. and Mortensen, D.A. (1995) Shape features for identifying young weeds using image analysis. Trans. ASAE 38:271-281.
50. Zhang, N. and Chaisattapagon, C. (1995) Effective criteria for weed identification in wheat fields using machine vision. Trans. ASAE 38:965-974.