TY - GEN
T1 - An algorithm to extract physical characteristics of nematodes from microscopic images of plant roots
AU - Toribio, Alexis
AU - Vargas, Luis
AU - Kemper, Guillermo
AU - Palomo, Alfonso
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In Peru, phytoparasite nematodes are a type of specimen that adheres to vegetable roots, preventing plants from absorbing the necessary nutrients to grow. In order to apply contingency and prevention plans, it is necessary to study the families to which the nematode pests belong by analyzing their physical characteristics with a microscope. The proposed system represents an alternative to the current methods for the classification of phytoparasite nematodes through image processing. This system is an algorithm oriented to detect the physical characteristics of nematodes in tropical fruit crops (width and length). The algorithm involves image acquisition of nematodes through a microscope, obtain the luminance component of the image, illumination correction, binarization by histogram, objects segmentation, discrimination by area, surface detection and calculation of the Euclidean distance to approximate the physical characteristics of specimens in a sample of nematodes. Favorable results were obtained in the detection of the characteristics of the Meloidogyne type II species. The results were validated from those obtained by clinical analysis of the specialists, achieving up to 85% of success.
AB - In Peru, phytoparasite nematodes are a type of specimen that adheres to vegetable roots, preventing plants from absorbing the necessary nutrients to grow. In order to apply contingency and prevention plans, it is necessary to study the families to which the nematode pests belong by analyzing their physical characteristics with a microscope. The proposed system represents an alternative to the current methods for the classification of phytoparasite nematodes through image processing. This system is an algorithm oriented to detect the physical characteristics of nematodes in tropical fruit crops (width and length). The algorithm involves image acquisition of nematodes through a microscope, obtain the luminance component of the image, illumination correction, binarization by histogram, objects segmentation, discrimination by area, surface detection and calculation of the Euclidean distance to approximate the physical characteristics of specimens in a sample of nematodes. Favorable results were obtained in the detection of the characteristics of the Meloidogyne type II species. The results were validated from those obtained by clinical analysis of the specialists, achieving up to 85% of success.
KW - Illumination correction
KW - image processing
KW - microscopic images
KW - nematodes
KW - plant roots
UR - https://www.scopus.com/pages/publications/85062183640
U2 - 10.1109/ICA-ACCA.2018.8609756
DO - 10.1109/ICA-ACCA.2018.8609756
M3 - Contribución a la conferencia
AN - SCOPUS:85062183640
T3 - IEEE ICA-ACCA 2018 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control: Towards an Industry 4.0 - Proceedings
BT - IEEE ICA-ACCA 2018 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control
A2 - Duran-Faundez, Cristian
A2 - Lefranc, Gaston
A2 - Fernandez-Fernandez, Mario
A2 - Munoz, Carlos
A2 - Rubio, Ernesto
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control: Towards an Industry 4.0, ICA-ACCA 2018
Y2 - 17 October 2018 through 19 October 2018
ER -