A review on computer vision technologies applied in greenhouse plant stress detection

Communications in Computer and Information Science

Volumn 363, Issue , 2013, Pages 192-200

  Lin, Kaiyan ^a,b   Chen, Jie ^a,b   Si, Huiping ^a,b   Wu, Junhui ^a,b  

^a TONGJI UNIVERSITY   (China)

^b Tongji University   (China)

Author keywords

Computer vision; Feature extraction; Feature selection; Plant segmentation; Plant stress

Indexed keywords

COMPUTER VISION; FEATURE EXTRACTION;

COMPUTER VISION SYSTEM; COMPUTER VISION TECHNOLOGY; DETECTION APPROACH; GREEN PLANTS; NATURAL LIGHT; NUTRIENT STRESS; PLANT STRESS; WATER STRESS;

STRESSES;

EID: 84882276930     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-37149-3_23     Document Type: Conference Paper

References (38)

1. Ondimu, S.N., Murase, H.: Comparison of plant water stress detection ability of color and gray level texture in sunagoke moss. Transactions of the ASABE 51(3), 1111-11120 (2008)
2. Parsons, N.R., Edmondsona, R.N., Song, Y.: Image analysis and statistical modeling for measurement and quality assessment of ornamental horticulture crops in glasshouses. Biosystems Engineering 104, 161-168 (2009)
3. Woebbecke, D.M., Meyer, G.E., Bargen, K.V., Mortensen, D.A.: Color indices for weed identification under various soil residue and lighting conditions. Transactions of the ASAE 38, 259-269 (1995)
4. Perez, A.J., Lopez, F., Benlloch, J.V., Christensen, S.: Color and shape analysis techniques for weed detection in cereal fields. Computer and Electronics in Agriculture 25, 197-212 (2000)
5. Kataoka, T., Kaneko, T., Okamoto, H., et al.: Crop growth estimation system using machine vision. In: Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1079-1083 (2003)
6. Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Computer and Electronics in Agriculture 63, 282-293 (2008)
7. Guijarroa, M., Pajaresb, G., Riomorosc, I.: Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture 75, 75-83 (2011)
8. Zheng, L.Y., Zhang, J.T., Wang, Q.Y.: Mean-shift-based color segmentation of images containing green vegetation. Computer and Electronics in Agriculture 65, 93-98 (2009)
9. Arribas, J.I., Sánchez-Ferrero, G.V., Ruiz-Ruiz, G., et al.: Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture 78, 9-18 (2011)
10. Nilsson, H.: Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology 15, 489-527 (1995)
11. Wang, D., Gartung, J.: Infrared canopy temperature of early-ripening peach trees under postharvest deficit irrigation. Agricultural Water Management 97, 1787-1794 (2010)
12. Seginer, I., Elster, R.T., Goodrum, J.W., Rieger, M.W.: Plant wilt detection by computervision tracking of leaf tips. Transactions of the ASABE 35, 1563-1567 (1992)
13. Kurata, K., Yan, J.: Water stress estimation of tomato canopy based on machine vision. Acta Horticulturae 440, 389-394 (1996)
14. Revollon, P., Chasseriaux, G., Riviere, L.M., et al.: The use of image processing for tracking the morphological modification of Forsythia following an interruption of watering. In: Proc. International Conference on Agricultural Engineering, AgEng OSLO 1998, Oslo, Norway, August 24-27, pp. 872-873 (1998)
15. Kacira, M., Ling, P.P., Short, T.H.: Machine vision extracted plant movement for early detection of plant water stress. Transactions of the ASAB 45(4), 1147-1153 (2002)
16. Foucher, P., Revollon, P., Vigouroux, B., Chasseriaux, G.: Morphological image analysis for the detection of water stress in potted forsythia. Biosystems Engineering 89(2), 131-138 (2004)
17. Hendrawan, Y., Murase, H.: Bio-inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss. Expert Systems with Applications (38), 14321-114335 (2011)
18. Kim, Y., Glenn, D.M., Park, J., et al.: Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture 77, 155-160 (2011)
19. Mercure, S.A., Daoust, B., Samson, G.: Causal relationship between growth inhibition, accumulation of phenolic metabolites, and changes of UV-induced fluorescences in nitrogen-deficient barley plants. Can. J. Bot. 82, 815-821 (2004)
20. Xu, G.L., Zhang, F.L., Shah, S.G., et al.: Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recognition Letters 32, 1584-1590 (2011)
21. Hetzroni, A., Miles, G.E., Engel, B.A., et al.: Machine vision monitoring of plant health. Advances in Space Research 14(11), 203-212 (1994)
22. Ahmad, I.S., Reid, J.F.: Evaluation of colour representations for maize images. Journal of Agricultural and Engineering Research 63, 185-196 (1996)
23. Li, C.Y., Teng, G.H., Zhao, C.J., et al.: Development of non-contact measurement on plant growth in greenhouse using computer vision. Transactions of the CSAE 19(3), 140-143 (2003)
24. Mao, H.P., Li, P.P., Xu, G.L.: Diagnosis of nutrient deficiency of tomato based on computer vision. Transactions of the Chinese Society for Agricultural Machinery 34(2), 73-75 (2003)
25. Lei, Y.W., Wei, C.Z., Ye, J., et al.: Application of computer-aided cotton leaf-color analysis in nitrogen status diagnosis in cotton plants. J. Shihezi Univ. (Nat. Sci.) 22(2), 113-116 (2004)
26. Zhang, Y.E., Li, M.Z., Zhang, X.J., et al.: Nutrition information extraction of the cucumber leaves in the greenhouse based on computer vision technology. Transactions of the CSAE 21(8), 102-105 (2005)
27. Story, D., Kacira, M., Kubota, C., et al.: Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture 74, 238-243 (2010)
28. Hanafi, A.: Integrated production and protection today and in the future in greenhouse crops in the Mediterranean region. Acta Horticulturae (ISHS) (614), 755-765 (2003)
29. Sasaki, Y., Kamoto, T.: Automatic Diagnosis of Plant Disease. Journal of JSAM 61(2), 119-126 (1999)
30. Chen, J.J., Ji, S.W., Li, J., et al.: Automatic measurement of danger degree of cotton insect pests using computer vision. Transactions of the CSAE 17(2), 157-160 (2001)
31. Moya, E.A., Barrales, L.R., Apablaza, G.E.: Assessment of the disease severity of squash powdery mildew through visual analysis, digital image analysis and validation of these methodologies. Crop Prot. 24(9), 785-789 (2005)
32. Bauch, C., Rath, T.: Prototype of a vision based system for measurements of whitefly infestation. Acta Horticulturae (ISHS) (691), 773-780 (2005)
33. Cui, Y.L., Cheng, P.F., Dong, X.Z., et al.: Image processing and extracting color features of greenhouse diseased leaf. Transactions of the CSAE, Supp., 32-35 (2005)
34. Pydipati, R., Burks, T.F., Lee, W.S.: Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture 52, 49-59 (2006)
35. Zhao, Y.X., Wang, K.R., Bai, Z.Y.: Research of maize leaf disease identifying system based image recognition. Sci. Agric. Sin. 40(4), 698-703 (2007)
36. Huang, K.Y.: Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in Agriculture 57, 3-11 (2007)
37. Boissard, P., Martin, V., Moisan, S.: A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture 62, 81-93 (2008)
38. Mao, H.P., Zhang, Y.C., Hu, B.: Segmentation of crop disease leaf images using fuzzy Cmeans clustering algorithm. Transactions of the CSAE 24(9), 136-140 (2008)