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Classification of pest detection in paddy crop based on transfer learning approach
V. Malathi,
Published in Taylor and Francis Ltd.
Pest recognition in the agriculture field plays a critical issue for the farmers which diminishes economic growth. So far, traditional practices were followed by farmers to increase yield production. Nowadays, researchers execute a deep learning approach to classify various kinds of images practically. In this paper, Deep convolutional neural networks (DCNN) are used to recognise ten kinds of pests present in the paddy crop. The data repository contains around 3549 pest images that affect the paddy crops, Since Deep Learning supports well for larger data-set so the data augmentation process is carried out. The neural model is build using various kinds of DCNN architecture, interpretation was made over the models based on the accuracy rate and the performance. The transfer learning approach is applied over the pest data set by fine-tuning the hyperparameters and the layers of the ResNet-50 model. By comparing the resultant value, the fine-tuned ResNet-50 model produced better accuracy of 95.012% among other models. The obtained resultant value describes the effective performance of the model in pest disease classification. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
About the journal
JournalData powered by TypesetActa Agriculturae Scandinavica Section B: Soil and Plant Science
PublisherData powered by TypesetTaylor and Francis Ltd.