Scale invariant feature approach for insect monitoring

Computers and Electronics in Agriculture

Volumn 75, Issue 1, 2011, Pages 92-99

  Solis Sánchez, Luis O ^a,b   Castañeda Miranda, Rodrigo ^b,c   García Escalante, Juan J ^c   Torres Pacheco, Irineo ^b   Guevara González, Ramón G ^b   Castañeda Miranda, Celina L ^b   Alaniz Lumbreras, Pedro D ^a  

^a UNIVERSIDAD AUTÓNOMA DE ZACATECAS   (Mexico)

^b UNIVERSIDAD AUTÓNOMA DE QUERÉTARO   (Mexico)

^c Laboratory of Biotronics   (Mexico)

Author keywords

Greenhouse; IPM; Machine vision; Precision agriculture; SIFT

Indexed keywords

APHIS GOSSYPII; APPLICATION OF THE IMAGE PROCESSING; CHRYSOMELIDAE; COLEOPTERA; CORRECTIVE MEASURES; DETERMINATION COEFFICIENTS; GREENHOUSE CROP PRODUCTION; IN-FIELD; INTEGRATED PEST MANAGEMENT; IPM; MACHINE VISION; MACHINE VISION SYSTEMS; PRECISION AGRICULTURE; PRODUCTION SYSTEM; SCALE INVARIANT; SCALE INVARIANT FEATURE TRANSFORMS; SIFT;

ALGORITHMS; CROPS; CULTIVATION; GREENHOUSES; PEST CONTROL;

COMPUTER VISION;

ALGORITHM; APHID; BEETLE; CROP PRODUCTION; GREENHOUSE ECOSYSTEM; INSECT; INTEGRATED PEST MANAGEMENT; MONITORING; THRIPS; TRAP (EQUIPMENT); WHITEFLY;

ALEYRODIDAE; APHIDIDAE; APHIS GOSSYPII; BEMISIA TABACI; CHRYSOMELIDAE; COLEOPTERA; DIABROTICA; EMPOASCA; HEXAPODA; NEUROPTERA; THRIPS TABACI;

EID: 78650514774     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2010.10.001     Document Type: Article

References (23)

1. Allen W.A., Rajotte E.G. The changing role of extension entomology in the IPM era. Annual Review of Entomology 1990, 35:379-397.
2. Boissard P., Martin V., Moisan S. A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture 2008, 62(2):81-93.
3. Dent D. Insect Pest Management 1999, CABI Publishing, pp. 14, 30. second ed.
4. Jahne B. Digital Image Processing 2005, Springer, pp. 506-513.
5. Kilian J. Simple Image Analysis By Moments 2001, (WWW document). http://vaderio.googlepages.com/SimpleImageAnalysisbyMoments.pdf.
6. Knight A., Hilton R., VanBuskirk P., Light D. Using Pear Ester to Monitor Codling Moth in Sex Pheromone Treated Orchards 2006, Oregon State University Extension Service EM 890, p. 8.
7. Koumpouros Y., Mahaman B.D., Maliappis M., Passam H.C., Sideridis A.B., Zorkadis V. Imagen processing for distance diagnosis in pest management. Computers and Electronics in Agriculture 2004, 44:121-131.
8. Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004, 60(2):91-110.
9. Neethirajan S., Karunakaran C., Jayas D.S., White N.D. Detection techniques for stored-product insects in grain. Food Control 2007, 18:157-162.
10. Papageorgiou C.P., Oren M., Poggio T. A general framework for object detection. Proceedings of the IEEE International Conference on Computer Vision 1998, 555.
11. Ridgway C., Davies E.R., Chambers J., Mason D.R., Bateman M. Rapid machine vision method for the detection of insects and other particulate biocontaminants of bulk grain in transit. Biosystems Engineering 2002, 83(1):21-30.
12. Smith R.H., Boutwell J.L., Allen J.W. Evaluating Practice Adoption: One Approach 1983, (WWW document). http://www.joe.org/joe/1983september/83-5-a5.pdf.
13. Smith R.F., Reynolds H.T. Principles, definitions and scope of integrated pest control. Proceedings of FAO (United Nations Food and Agriculture Organisation) Symposium on Integrated Pest Control 1 1966, 11-17.
14. Sobel, I., Feldman, G., 1973. A 3×3 Isotropic Gradient Operator for Image Processing, presented at a talk at the Stanford Artificial Project in 1968, unpublished but often cited, orig. in Pattern Classification and Scene Analysis, Duda, R. and Hart, P., John Wiley and Sons, pp. 271-272.
15. Solis-Sánchez L.O., García-Escalante J.J., Castañeda-Miranda R., Torres-Pacheco I., Guevara-González R.G. Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment. Journal of Applied Entomology 2009, 133(7):546-552.
16. Tilman D., Cassman K.G., Matson P.A., Naylor R., Polasky S. Agricultural sustainability and intensive production practices. Nature 2002, 418:671-677.
17. Torralba A., Murphy K.P., Freeman W.T. Sharing features: efficient boosting procedures for multiclass object detection. Proceedings of the 18th IEEE Conference on Computer Vision and Pattern Recognition 2004, 762-769.
18. Vale G.A. The trap-orientated behaviour tsetse flies (Glossinidae) and other Diptera. Bulletin of Entomological Research 1982, 72:71-93.
19. Villamizar, M., Sanfeliu, A., 2006. Cómputo de Características Invariantes a la Rotación para el Reconocimiento de Distintas Clases de Objetos. [WWW document]. URL , XXVII Jornadas de Automatica, Almería 2006 - ISBN: 84-689r-r9417-0. http://www.iri.upc.edu/groups/lrobots/publications/villamizar_ja06.pdf.
20. Viola P., Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the 15th IEEE Conference on Computer Vision and Pattern Recognition 2001, 511-518.
21. Ward S.A., Rabbinge R., Mantel W.P. The use of incidence counts for estimation of aphid populations. 1. Minimum sample size for required accuracy. Netherlands Journal of Plant Pathology 1985, 91:93-99.
22. Wise J.C., Gut L.J., Isaacs R. Michigan Fruit Management Guide 2007, Department of Plant Pathology and Department of Horticulture, Michigan State University.
23. Yokono, J.J., Poggio, T., 2004. Rotation invariant object recognition from one training example. Technical Report 2004-010, MIT AI Lab.