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• W3033460776 abstract "Country olive forests are one of the major contributors with economic aspects. Spain is the leading country in the world which produced around 45% of olive oil compared to the total production in the world. The value of 2.4 million hectares of land is dedicated to olive cultivation in the country, which made it a huge olive oil producer in the world. Olive crop widely spread over large extensive areas; manual counting of trees is humanly infeasible. To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. The single red band once extracted from the color spectrum of acquired images, is then sharpened and edges are detected. The closed edges formed by the tree boundaries are transformed into white blobs using morphological reconstruction. Resulting circular blobs are then filtered out based on their shape and size. Blobs with circular geometry and in-range radius are considered as olive trees, which are then mapped with the existing ground information. Results have been generated over the diverse images capturing the ground truth information with an estimation error of 1.27%." @default.
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• W3033460776 date "2020-01-01" @default.
• W3033460776 modified "2023-09-28" @default.
• W3033460776 title "An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing" @default.
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• W3033460776 doi "https://doi.org/10.1109/access.2020.2999078" @default.
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