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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
arts and humanities
Agriculture satellite image segmentation using a modified artificial Hopfield neural network
Computers in Human Behavior, Volume 30, Year 2014
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Description
Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region's relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images. © 2013 Elsevier Ltd. All rights reserved.
Authors & Co-Authors
Sammouda, Rachid
Saudi Arabia, Riyadh
College of Sciences
Adgaba, Nuru
Saudi Arabia, Riyadh
King Saud University
Touir, Ameur
Saudi Arabia, Riyadh
College of Sciences
Alghamdi, Abdullah A.
Saudi Arabia, Riyadh
King Saud University
Statistics
Citations: 45
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.1016/j.chb.2013.06.025
ISSN:
07475632
Study Design
Cross Sectional Study