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primary topic: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants
type: AcademicArticle
type: Article
abstract: The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. Its rapid and destructive spread has turned it into a major global threat, causing widespread damage that continues to grow with time. Countries like South Africa, the United States, and Australia have implemented extensive measures to control the spread of PSHB, including the establishment of specialized agricultural support centers for early detection. However, there is still a strong need to make PSHB detection more accessible, allowing even non-experts to easily identify infections at an early stage. Artificial Intelligence (AI) has shown great promise in plant disease detection, but a major challenge in the case of PSHB was the lack of a suitable dataset for training AI models. In the proposed work, we first created a dedicated dataset by collecting images of trees infected with PSHB. We applied a range of preprocessing techniques to refine the dataset and prepare it for AI applications. Building on this, we developed a novel AI-based method, where we trained a deep learning model using a multi-convolutional layer network combined with a Fourier transformation layer. Additionally, an attention mechanism and advanced feature extraction techniques were incorporated to further boost model performance. As a result, the proposed approach achieved an impressive top accuracy of 92.3% in detecting PSHB infections, showing the potential of AI to offer a simple, efficient, and highly accurate solution for early disease detection.
date: 2026-04
is part of: BMC Plant Biology
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Texto)
has document: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Otro)
subject: Engineering

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Texto)
type: Document
has file: s12870-026-08847-6_reference.pdf
has part: s12870-026-08847-6_reference.pdf

BMC Plant Biology
type: Collection
issn: 14712229

Gyu Sang Choi
type: Person
given name: Gyu Sang
family name: Choi

Rabbiya Younas
type: Person
given name: Rabbiya
family name: Younas

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Hafiz Muhammad Raza ur Rehman
type: Person
given name: Hafiz Muhammad Raza
family name: ur Rehman

Carlos Eduardo Uc Ríos
type: Person
given name: Carlos Eduardo
family name: Uc Ríos

Ángel Gabriel Kuc Castilla
type: Person
given name: Ángel Gabriel
family name: Kuc Castilla

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Otro)
type: Document
is version of: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Texto)
is volatile version of: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants (Texto)
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primary topic: An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants

Engineering
type: Concept
preferred label: Ingeniería @es