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primary topic: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification
type: AcademicArticle
type: Article
abstract: Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design ena…
date: 2026-01
volume: 16
is part of: Frontiers in Plant Science
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Texto)
has document: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Otro)
subject: Engineering

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Texto)
type: Document
has file: fpls-16-1720471.pdf
has part: fpls-16-1720471.pdf

Frontiers in Plant Science
type: Collection
issn: 1664462X

Amna Ikram
type: Person
given name: Amna
family name: Ikram

Luis Alonso Dzul López
type: Person
given name: Luis Alonso
family name: Dzul López

Irene Delgado Noya
type: Person
given name: Irene
family name: Delgado Noya

Rupesh Gupta
type: Person
given name: Rupesh
family name: Gupta

Fuad Ali Mohammed Al-Yarimi
type: Person
given name: Fuad Ali Mohammed
family name: Al-Yarimi

Jeewan Singh
type: Person
given name: Jeewan
family name: Singh

Ateeq Ur Rehman
type: Person
given name: Ateeq Ur
family name: Rehman

Gaurav Tuteja
type: Person
given name: Gaurav
family name: Tuteja

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Otro)
type: Document
is version of: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Texto)
is volatile version of: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Texto)
is index codes version of: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

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primary topic: CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification

Engineering
type: Concept
preferred label: Ingeniería @es