Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Varsha Kiran Bhosale
National Kaohsiung University of Science and Technology
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging Varsha Kiran Bhosale; Chin-Shiuh Shieh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1364-1375

Abstract

Strawberries are severely affected by the main fungal diseases such as anthracnose fruit rot, grey mould, and powdery mildew, directly reducing commercial yield and post-harvest quality. In this paper, we propose an enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields. This dataset contains images acquired from both public domain repositories as well as farm operational settings, encompassing three classes of diseases with varying lighting conditions, background noise, and occlusion. A structured pre-processing workflow comprising contrast enhancement, denoising, and synthetic hyperspectral simulation is used to enhance subtle lesion features and stabilize the subsequent feature extraction. The hybrid GNN–MLP framework possesses the merits of spatially local lesion topology and two-point contextual information, which can improve disease classification compared with common CNN-based structures. 5-fold cross-validation shows that the model holds 93.59% accuracy and a macro F1-score of 90.39%, showing good generalization even with heterogeneous input regimes. Transparent models use local interpretable model-agnostic explanations (LIME) and gradient-weighted class activation mapping (Grad-CAM) in combination, highlighting disease-relevant areas that give an interpretable rationale for each prediction. To conclude, the developed system offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.
Co-Authors Chin-Shiuh Shieh