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14.08.2022

Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks

verfasst von: Md Sakib Ullah Sourav, Huidong Wang

Erschienen in: Neural Processing Letters

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Abstract

Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't always accurate. We developed an intelligent model for jute pests identification based on transfer learning (TL) and deep convolutional neural networks (DCNN) to solve this practical problem. The proposed DCNN model can realize fast and accurate automatic identification of jute pests based on photographs. Specifically, the VGG19 CNN model was trained by TL on the ImageNet database. A well-structured image dataset of four dominant jute pests is also established. Our model shows a final accuracy of 95.86% on the four most vital jute pest classes. The model’s performance is further demonstrated by the precision, recall, F1-score, and confusion matrix results. The proposed model is integrated into Android and IOS applications for practical uses.
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Metadaten
Titel
Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks
verfasst von
Md Sakib Ullah Sourav
Huidong Wang
Publikationsdatum
14.08.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10978-4