Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2093
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dc.contributor.authorDavid, Hepzibah Elizabeth-
dc.contributor.authorRamalakshmi, K-
dc.contributor.authorVenkatesan, R-
dc.contributor.authorHemalathad, G-
dc.date.accessioned2023-11-27T14:54:30Z-
dc.date.available2023-11-27T14:54:30Z-
dc.date.issued2021-10-04-
dc.identifier.issn0927-5452-
dc.identifier.issn1879-808X-
dc.identifier.urihttps://doi.org/10.3233/apc210108-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2093-
dc.description.abstractTomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.en_US
dc.language.isoenen_US
dc.publisherAdvances in Parallel Computingen_US
dc.subjectCNN-RNN modelen_US
dc.subjectDiseasesen_US
dc.subjectTomato leaf diseaseen_US
dc.subjectTomato cropsen_US
dc.subjectLong-Short Term Memory (LSTMs)en_US
dc.titleTomato Leaf Disease Detection Using Hybrid CNN-RNN Modelen_US
dc.typeArticleen_US
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