Original paper: Scale invariant feature approach for insect monitoring

One of the main problems in greenhouse crop production is the presence of pests. In order to address this problem, the implementation of a Integrated Pest Management (IPM) system involving the detection and classification of insects (pests) is essential for intensive production systems. Traditionally, this has been done by placing hunting traps in fields or greenhouses and later manually counting and identifying the insects found. This is a very time-consuming and expensive process. To facilitate this process, it is possible to use machine vision techniques. This work describes an application of the machine vision system LOSS V2 algorithm, an expanded version of the LOSS algorithm discussed in a previous work by the same authors. This expanded version demonstrated improved potential and was used to detect and identify the following pest species: Diabrotica (Coleoptera: Chrysomelidae), Lacewings (Lacewings spp.), Aphids (Aphis gossypii Genn.), Glassy (Empoasca spp.), Thrips (Thrips tabaci L.), and Whitefly (Bemisia tabaci Genn.). The algorithm identifies pest presence in the crop and makes it possible for the greenhouse manager to take the appropriate preventive or corrective measures. The LOSS V2 involves the application of the LOSS algorithm for initial pest identification, followed by the application of the image processing technique known as scale invariant feature transform (SIFT). This allows for more accurate pest detection because it is possible to discriminate and identify different types of insects. Therefore, when compared to manual pest counting, the newly developed LOSS V2 algorithm showed more precision in identifying different pest varieties, and also, a much higher determination coefficient, R^2=0.99.

[1]  Edwin G. Rajotte,et al.  The Changing Role of Extension Entomology in the IPM ERA , 1990 .

[2]  Noel D.G. White,et al.  Detection techniques for stored-product insects in grain , 2007 .

[3]  G. Vale.  The trap-orientated behaviour of tsetse flies (Glossinidae) and other Diptera , 1982, Bulletin of Entomological Research.

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Rodrigo Castañeda-Miranda,et al.  Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment , 2009 .

[6]  S. Polasky,et al.  Agricultural sustainability and intensive production practices , 2002, Nature.

[7]  David Dent,et al.  Insect Pest Management , 1991 .

[8]  Miroslaw Pawlak,et al.  On Image Analysis by Moments , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R. Rabbinge,et al.  The use of incidence counts for estimation of aphid populations. 1. Minimum sample size for required accuracy , 1985, Netherlands Journal of Plant Pathology.

[11]  Michael T. Maliappis,et al.  Image processing for distance diagnosis in pest management , 2004 .

[12]  Tomaso Poggio,et al.  Rotation Invariant Object Recognition from One Training Example , 2004 .

[13]  Vincent Martin,et al.  A cognitive vision approach to early pest detection in greenhouse crops , 2008 .

[14]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[15]  E. R. Davies,et al.  AE—Automation and Emerging Technologies: Rapid Machine Vision Method for the Detection of Insects and other Particulate Bio-contaminants of Bulk Grain in Transit , 2002 .

[16]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.