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An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels

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@article{IJASEIT5322,
   author = {Itamar F. Salazar-Reque and Samuel Gustavo Huamán and Guillermo Kemper and Joel Telles and Daniel Diaz},
   title = {An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {194--203},
   keywords = {segmentation; superpixels; leaf diseases.},
   abstract = {Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5322},
   doi = {10.18517/ijaseit.9.1.5322}
}

EndNote

%A Salazar-Reque, Itamar F.
%A Huamán, Samuel Gustavo
%A Kemper, Guillermo
%A Telles, Joel
%A Diaz, Daniel
%D 2019
%T An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels
%B 2019
%9 segmentation; superpixels; leaf diseases.
%! An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels
%K segmentation; superpixels; leaf diseases.
%X Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5322
%R doi:10.18517/ijaseit.9.1.5322
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 1
%@ 2088-5334

IEEE

Itamar F. Salazar-Reque,Samuel Gustavo Huamán,Guillermo Kemper,Joel Telles and Daniel Diaz,"An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 194-203, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.5322.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Salazar-Reque, Itamar F.
AU  - Huamán, Samuel Gustavo
AU  - Kemper, Guillermo
AU  - Telles, Joel
AU  - Diaz, Daniel
PY  - 2019
TI  - An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 194
EP  - 203
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - segmentation; superpixels; leaf diseases.
N2  - Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5322
DO  - 10.18517/ijaseit.9.1.5322

RefWorks

RT Journal Article
ID 5322
A1 Salazar-Reque, Itamar F.
A1 Huamán, Samuel Gustavo
A1 Kemper, Guillermo
A1 Telles, Joel
A1 Diaz, Daniel
T1 An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 194
OP 203
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 segmentation; superpixels; leaf diseases.
AB Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5322
DO  - 10.18517/ijaseit.9.1.5322