An Automated Framework of Superpixels-Saliency Map and Gated Recurrent Unit Deep Convolutional Neural Network for Land Cover and Crops Disease Classification

An Automated Framework of Superpixels-Saliency Map and Gated Recurrent Unit Deep Convolutional Neural Network for Land Cover and Crops Disease Classification

In this work, we proposed an automated deep learning and saliency map architecture for the segmentation of crops, leaf disease segmentation, and land cover classification. The proposed framework is based on two embedded steps. In the first step, crop leaf disease segmentation was performed using sup...

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Bibliographic Details
Main Authors: Irfan Haider, Muhammad Attique Khan, Muhammad Nazir, Saleha Masood, Naoufel Kraiem, Dina Abdulaziz Alhammadi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Cucumber leaf disease
contrast enhancement
superpixel clustering
saliency map
deep learning
optimization
Online Access:https://ieeexplore.ieee.org/document/10965697/
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