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Menampilkan 1–2 dari 2 artikel
A Lightweight Maize Leaf Disease Recognition Using PCA-Compressed MobileNetV2 Features and RBF-SVM
Abubakar, Mustapha
; Ibrahim, Yusuf
; Ajayi, Ore-Ofe
; Saminu, Sani Saleh
Journal of Computing Theories and Applications
Vol 3
, No 3
(2026)
The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classificat...
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EDANet: A Novel Architecture Combining Depthwise Separable Convolutions and Hybrid Attention for Efficient Tomato Disease Recognition
Ibrahim, Yusuf
; O. Momoh, Muyideen
; O. Shobowale, Kafayat
; Mukhtar Abubakar, Zainab
; Yahaya, Basira
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to pr...
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