BouachirIhouGueziriEtAl2019

Reference

Bouachir, W., Ihou, K.E., Gueziri, H.-E., Bouguila, N., Belanger, N. (2019) Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery. IEEE Access, 7:82491-82500. (Scopus )

Abstract

Mechanical site preparation by mounding is often used by the forest industry to provide optimal growth conditions for tree seedlings. Prior to planting, an essential step consists in estimating the number of mounds at each planting block, which serves as planting microsites. This task often requires long and costly field surveys, implying several forestry workers to perform manual counting procedure. This paper addresses the problem of automating the counting process using computer vision and UAV imagery. We present a supervised detection-based counting framework for estimating the number of planting microsites on a mechanically prepared block. The system is trained offline to learn feature representations from semi-automatically annotated images. Mound detection and counting are then performed on multispectral UAV images captured at an altitude of 100 m. Our detection framework proceeds by generating region proposals based on local binary patterns (LBP) features extracted from near-infrared (NIR) patches. A convolutional neural network (CNN) is then used for classifying candidate regions by considering multispectral image data. To train and evaluate the proposed method, we constructed a new dataset by capturing aerial images from different planting blocks. The results demonstrate the efficiency and validity of the proposed method under challenging experimental conditions. The methods and results presented in this paper form a promising cornerstone to develop advanced decision support systems for planning planting operations. © 2013 IEEE.

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@ARTICLE { BouachirIhouGueziriEtAl2019,
    AUTHOR = { Bouachir, W. and Ihou, K.E. and Gueziri, H.-E. and Bouguila, N. and Belanger, N. },
    TITLE = { Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery },
    JOURNAL = { IEEE Access },
    YEAR = { 2019 },
    VOLUME = { 7 },
    PAGES = { 82491-82500 },
    NOTE = { cited By 0 },
    ABSTRACT = { Mechanical site preparation by mounding is often used by the forest industry to provide optimal growth conditions for tree seedlings. Prior to planting, an essential step consists in estimating the number of mounds at each planting block, which serves as planting microsites. This task often requires long and costly field surveys, implying several forestry workers to perform manual counting procedure. This paper addresses the problem of automating the counting process using computer vision and UAV imagery. We present a supervised detection-based counting framework for estimating the number of planting microsites on a mechanically prepared block. The system is trained offline to learn feature representations from semi-automatically annotated images. Mound detection and counting are then performed on multispectral UAV images captured at an altitude of 100 m. Our detection framework proceeds by generating region proposals based on local binary patterns (LBP) features extracted from near-infrared (NIR) patches. A convolutional neural network (CNN) is then used for classifying candidate regions by considering multispectral image data. To train and evaluate the proposed method, we constructed a new dataset by capturing aerial images from different planting blocks. The results demonstrate the efficiency and validity of the proposed method under challenging experimental conditions. The methods and results presented in this paper form a promising cornerstone to develop advanced decision support systems for planning planting operations. © 2013 IEEE. },
    AFFILIATION = { Department of Science and Technology, TÉLUQ University, Montreal, QC G1K 9H6, Canada; Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada },
    ART_NUMBER = { 8740862 },
    AUTHOR_KEYWORDS = { artificial intelligence; computer vision; Precision forestry; UAV imagery },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1109/ACCESS.2019.2923765 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068711896&doi=10.1109%2fACCESS.2019.2923765&partnerID=40&md5=a44cf56e9969d1b1fb1999cf55a89d94 },
}

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