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Algorithm for Detection of Aonidiella aurantii in Citrus × tangelo Fruits using DIP Technique


  • Research Group Aplicabilidad Tecnologica, Research Center Manuela Beltran University, Av. Circunvalar No. 60-00, Bogota, Colombia


Objectives: To develop an algorithm to classify the fruit damage caused by the Aonidiella aurantii pest in a Citrus × tangelo crop. With this detection and prediction is intended to provide a tool for estimation of pesticide application doses on the affected plants. Methods/Statistical Analysis: A system based on digital image processing was implemented, to recognize the fruits that pass through a conveyor belt, thus to select the quality fruits. As of Digital techniques as 2D Fourier transform, for calculate the intensity changes on the image, digital filtering, Circle Hough transform as method to detect circular contours and HSB color model related to represent the colors based on three parameters: Hue, Saturation and Brightness. Findings: The algorithm was capable to identify and classify Citrus × Tangelo fruits, using as parameter the recognized affected surface. After the image segmentation, a new mask was applied to recognize the free A. aurantii area of the fruit, as of second histogram information, was possible to obtain the healthy area of the fruit. One of the factors to obtain a feasible identification of the health areas on the fruits was the quantity and quality was the light in the work environment, due to a degradation of these factors could lead to a bad interpretation of the data when reflections or shadows are present. Application/Improvements: The main contribution of this system is the implementation of artificial vision methods for detecting affected areas on the surface of the fruit and determines its quality in order to accomplish the task of fruit selection. Also this work contributes to the development of agro industry, proposing new non-invasive methodologies to improve the production process in the agricultural sector in Colombia.


Aonidiella aurantii, Artificial Vision, Detection Algorithm, DIP Techniques, Orange Crops.

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  • Climent JML. Homoptera I: cochinillas de los citricos y su control biological. Pisa Edition. 1990.
  • Stenberg JA, Heil M, Ahman I, Bjorkman C. Optimizing crops for biocontrol of pests and disease. Trends Plant Science. 2015; 20(11): 698–712. PMid: 26447042. Crossref
  • Hartman GL, Pawlowski Ml, Chang HX, Hill CB. Emerging technologies for promoting food security. Elsevier. 2016.
  • Zhao C, Wu H. Research on the diagnosis method of crop pests and diseases based on the heuristic search. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. 2009; 2:349–56. Crossref
  • Wu D, Sun DW. Colour measurements by computer vision for food quality control – A review. Trends Food Science Technology. 2013; 29(1):5–20. Crossref
  • Blasco J, Aleixos N, Gomez J, Molto E. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering. 2007; 83(3):384–93. Crossref
  • Omid M, Khojastehnazhand M, Tabatabaeefar A. Estimating volume and mass of citrus fruits by image processing technique. Journal of Food Engineering. 2010; 100(2):315– 21. Crossref
  • Lopez-Garcia F, Andreu-Garcia G, Blasco J, Aleixos N, Valiente JM. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture. 2010; 71(2):189–97. Crossref
  • Adeva JJG, Reynolds M. Web-based simulation of fruit fly to support biosecurity decision-making. Ecological Informatics. 2012; 9:19–36. Crossref
  • Ren D, Yu H, Fu W, Zhang B, Ji Q. Crop diseases and pests monitoring based on remote sensing: A survey. World Automation Congress (WAC); 2012. p. 177–81. PMCid: PMC4093133.
  • Park C. 2D discrete Fourier transform on sliding windows. IEEE Transaction Image Processing. 2015; 24(3): 901–7. PMid: 25585421. Crossref
  • Coumar SO, Rajesh P, Sadanandam S. Image restoration using filters and image quality assessment using reduced reference metrics. 2013 International Conference on Circuits, Controls and Communications (CCUBE); 2013. p. 1–5.
  • Mukhopadhyay P, Chaudhuri B. A survey of Hough Transform. Pattern Recognition. 2015; 48(3):993–1010. Crossref
  • Bracamontes EAM, Rosas MEM, Velasco MMM, Reyes HLM, Sandoval JRM, Avila HCD. Implementation of Hough transform for fruit image segmentation. Procedia Eng. 2012; 35:230–9. Crossref
  • Samanta D, Sanyal G. A novel approach of SAR image processing based on Hue Saturation and Brightness (HSB). Procedia Technology. 2012; 4:584–8. Crossref


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