Bridging the performance-efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection.
4 affiliations
- 1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
- 2Centre for Image and Vision Computing (CIVC), Centre of Excellence for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia.
- 3Berlin School of Business & Innovation (BSBI), Berlin, Germany.
- 4AI and Big Data Department, Woosong University, Daejeon, Republic of Korea.
Abstract
Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security. Conventional machine learning models often fail to generalize across diverse image conditions. For sustainable, field-deployable diagnostic systems, it is essential to close the performance-efficiency gap, as traditional deep learning methods require large amounts of training data and high-end hardware. We propose a hybrid deep learning machine learning (DL-ML) framework that has been tailored for accurate and resource-efficient jackfruit disease identification. This framework discusses deep visual representations that are extracted using a lightweight custom Convolutional Neural Network (CNN) combined with a previously trained EfficientNet-B0, capturing both localized and nested image features. Principal Component Analysis (PCA) is utilized for feature optimization to enhance discriminative power and eliminate redundancy, while SMOTE minimizes dataset imbalance by generating synthetic minority samples. These selected features are then classified by employing a variety of machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), XGBoost, CatBoost, and k-Nearest Neighbors (KNN). A stacked ensemble model incorporates the strengths of these classifiers, enhancing generalization and robustness under diverse lighting and background conditions. Experiments across multiple dataset configurations show a noticeable improvement in accuracy from 76.80% on raw data to 99.39% on the PCA-augmented dataset, illustrating that this hybrid ensemble consistently outperforms all individual deep and machine learning baselines. Although the approach is currently tailored to jackfruit disease datasets, its applicability to other crops or stress conditions requires greater domain adaptation studies. However, the model's high accuracy highlights its scalability and utility in low-resource agricultural environments. Additionally, it establishes a cost-effective diagnostic paradigm where efficient ML classification and deep feature extraction collaborate, laying the foundation for next-generation smart farming and precision agriculture systems.
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BibTeX
@article{turjy2026bridging,
title = {{Bridging the performance-efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection.}},
author = {Rittik Chandra Das Turjy and Fahmid Al Farid and Md Hasan Imam Bijoy and Sarbajit Paul Bappy and Amir Sohel and Jia Uddin and Hezerul Bin Abdul Karim},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1884567},
url = {https://europepmc.org/article/MED/42903692},
note = {CC BY},
}
RIS
TY - JOUR
TI - Bridging the performance-efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection.
AU - Rittik Chandra Das Turjy
AU - Fahmid Al Farid
AU - Md Hasan Imam Bijoy
AU - Sarbajit Paul Bappy
AU - Amir Sohel
AU - Jia Uddin
AU - Hezerul Bin Abdul Karim
PY - 2026
DA - 2026/10/06
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1884567
UR - https://europepmc.org/article/MED/42903692
AB - Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security. Conventional machine learning models often fail to generalize across diverse image conditions. For sustainable, field-deployable diagnostic systems, it is essential to close the performance-efficiency gap, as traditional deep learning methods require large amounts of training data and high-end hardware. We propose a hybrid deep learning machine learning (DL-ML) framework that has been tailored for accurate and resource-efficient jackfruit disease identification. This framework discusses deep visual representations that are extracted using a lightweight custom Convolutional Neural Network (CNN) combined with a previously trained EfficientNet-B0, capturing both localized and nested image features. Principal Component Analysis (PCA) is utilized for feature optimization to enhance discriminative power and eliminate redundancy, while SMOTE minimizes dataset imbalance by generating synthetic minority samples. These selected features are then classified by employing a variety of machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), XGBoost, CatBoost, and k-Nearest Neighbors (KNN). A stacked ensemble model incorporates the strengths of these classifiers, enhancing generalization and robustness under diverse lighting and background conditions. Experiments across multiple dataset configurations show a noticeable improvement in accuracy from 76.80% on raw data to 99.39% on the PCA-augmented dataset, illustrating that this hybrid ensemble consistently outperforms all individual deep and machine learning baselines. Although the approach is currently tailored to jackfruit disease datasets, its applicability to other crops or stress conditions requires greater domain adaptation studies. However, the model's high accuracy highlights its scalability and utility in low-resource agricultural environments. Additionally, it establishes a cost-effective diagnostic paradigm where efficient ML classification and deep feature extraction collaborate, laying the foundation for next-generation smart farming and precision agriculture systems.
SN - 2624-8212
ER -