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Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks
(Canadian Science Publishing, 2024-04-10)
Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small-grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment, ...
Cryptographic Techniques for Data Privacy in Digital Forensics
(IEEE, 2023-12-15)
The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant ...
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
(Elsevier B.V., 2023-09-14)
Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the ...
Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification
(2022-05-11)
The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop ...
Perceptual image analysis
(Inderscience Enterprises Ltd., 2010)
The problem considered in this paper is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are qualitatively represented by feature-value vectors ...
3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems
(University of Winnipe, 2022-06)
3D signals have become widely popular in view of the advantage they provide via 3D representations of data by employing a third spatial or temporal dimension to extend 2D signals. Predominantly, 3D signals contain details ...
Perception-based image classification: Framework for perception-based cybernetics
(Emerald Insight, 2010-08-24)
Purpose: The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, ...
Inside out: transforming images of lab-grown plants for machine learning applications in agriculture
(Frontiers, 2023-07-06)
Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical ...
Automated LULC Map Production using Deep Neural Networks
(Taylor & Francis, 2019-01-17)
This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...
Automated Land Use and Land Cover Map Production: A Deep Learning Framework
(University of Winnipeg, 2018-10-19)
In this thesis, we present an approach to automating the creation of land use and land cover (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...