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Classification of Tomato Plants Diseases Using Convolutional Neural Network

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@article{IJASEIT11665,
   author = {I Ketut Gede Darma Putra and Rahmat Fauzi and Deden Witarsyah and I Putu Deva Jayantha Putra},
   title = {Classification of Tomato Plants Diseases Using Convolutional Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {1821--1827},
   keywords = {tomato diseases; deep learning; convolutional neural network; diseases recognition.},
   abstract = {

Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

},    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=11665},    doi = {10.18517/ijaseit.10.5.11665} }

EndNote

%A Darma Putra, I Ketut Gede
%A Fauzi, Rahmat
%A Witarsyah, Deden
%A Jayantha Putra, I Putu Deva
%D 2020
%T Classification of Tomato Plants Diseases Using Convolutional Neural Network
%B 2020
%9 tomato diseases; deep learning; convolutional neural network; diseases recognition.
%! Classification of Tomato Plants Diseases Using Convolutional Neural Network
%K tomato diseases; deep learning; convolutional neural network; diseases recognition.
%X 

Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11665 %R doi:10.18517/ijaseit.10.5.11665 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 5 %@ 2088-5334

IEEE

I Ketut Gede Darma Putra,Rahmat Fauzi,Deden Witarsyah and I Putu Deva Jayantha Putra,"Classification of Tomato Plants Diseases Using Convolutional Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 1821-1827, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.11665.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Darma Putra, I Ketut Gede
AU  - Fauzi, Rahmat
AU  - Witarsyah, Deden
AU  - Jayantha Putra, I Putu Deva
PY  - 2020
TI  - Classification of Tomato Plants Diseases Using Convolutional Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 1821
EP  - 1827
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - tomato diseases; deep learning; convolutional neural network; diseases recognition.
N2  - 

Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11665 DO - 10.18517/ijaseit.10.5.11665

RefWorks

RT Journal Article
ID 11665
A1 Darma Putra, I Ketut Gede
A1 Fauzi, Rahmat
A1 Witarsyah, Deden
A1 Jayantha Putra, I Putu Deva
T1 Classification of Tomato Plants Diseases Using Convolutional Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 1821
OP 1827
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 tomato diseases; deep learning; convolutional neural network; diseases recognition.
AB 

Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11665 DO - 10.18517/ijaseit.10.5.11665