Naufal, A. P. and Kanjanaphachoat, C. and Wijaya, A. and Setiawan, N. A. and Masithoh, R. E. (2021) Insects identification with convolutional neural network technique in the sweet corn field. IOP Conference Series: Earth and Environmental Science, 653 (1). ISSN 17551307
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Abstract
A method to identify the type of insects with accurate and precise results is of importance. Nowadays, an automatic object identification system with increased accuracy, improved speed, and less cost have been developed. Convolutional Neural Network (CNN) implementation for image identification or classification can be done by collecting large-scale datasets containing hundreds to millions of images to study the many parameters involved in the network. This research was conducted to develop and apply the CNN model to identify eight species of insects in the sweet corn field in Thailand. Those insects were Calomycterus sp., Rhopalosiphum maidis, Frankliniella williamsi, Spodoptera frugiperda, Spodoptera litura, Ostrinia furnacalis, Mythimna separata, and Helicoverpa armigera. The CNN model in this research was built with four convolutional layers, which consist of Conv2D, batch normalization, max pooling, dropout sublayer, and a fully-connected layer. in total, 5568 images were trained with 10 trials and different train attempts for each trial, were then tested with 40 images. The result shows that the CNN model has succeeded in identifying images of sweet corn insects with 80 up to 95 prediction accuracy for images with no background.
Item Type: | Article |
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Additional Information: | Library Dosen |
Uncontrolled Keywords: | Agriculture; Classification (of information); Convolution; Large dataset; Regional planning; Automatic object identification; Helicoverpa armigera; Image identification; Insects identifications; Large-scale datasets; Prediction accuracy; Spodoptera frugiperda; Spodoptera litura; Convolutional neural networks |
Subjects: | S Agriculture > S Agriculture (General) |
Divisions: | Faculty of Agricultural Technology > Agricultural and Biosystems Engineering |
Depositing User: | Sri JUNANDI |
Date Deposited: | 22 Oct 2024 04:15 |
Last Modified: | 22 Oct 2024 04:15 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/5446 |