干旱区生态环境知识资源中心(Arid): MULTI-PURPOSE CHESTNUT CLUSTERS DETECTION USING DEEP LEARNING: A PRELIMINARY APPROACH

Arid

DOI	10.5194/isprs-archives-XLII-3-W8-1-2019
	MULTI-PURPOSE CHESTNUT CLUSTERS DETECTION USING DEEP LEARNING: A PRELIMINARY APPROACH
	Adao, Telmo; Padua, Luis; Pinho, Tatiana M.; Hruska, Jonas; Sousa, Antonio; Sousa, Joaquim Joao; Morais, Raul; Peres, Emanuel
通讯作者	Adao, T (corresponding author), INESC Technol & Sci INESC TEC, Ctr Robot Ind & Intelligent Syst CRIIS, Porto, Portugal. ; Adao, T (corresponding author), Univ Tras Os Montes & Alto Douro, Sch Sci & Technol, Engn Dept, Vila Real, Portugal.
会议名称	Conference on Geo-information for Disaster Management (Gi4DM)
会议日期	SEP 03-06, 2019
会议地点	Prague, CZECH REPUBLIC
英文摘要	In the early 1980's, the European chestnut tree (Castanea sativa, Mill.) assumed an important role in the Portuguese economy. Currently, the Tras-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region ((SIC)50M-(SIC)60M). The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Tras-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease ( Phytophthora cinnamomi) and the chestnut blight (Cryphonectria parasitica), along with other threats, e.g. chestnut gall wasp (Dryocosmus kuriphilus) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation. Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera.
英文关键词	Chestnut Chestnut Tree Chestnut Detection Convolutional Neural Networks CNN Deep Learning DL Xception Rough Segmentation Tiling Segmentation
来源出版物	ISPRS ICWG III/IVA GI4DM 2019 - GEOINFORMATION FOR DISASTER MANAGEMENT
ISSN	1682-1750
EISSN	2194-9034
出版年	2019
卷号	42-3
期号	W8
页码	1-7
出版者	COPERNICUS GESELLSCHAFT MBH
类型	Proceedings Paper
语种	英语
开放获取类型	gold, Green Submitted
收录类别	CPCI-S ; CPCI-SSH
WOS记录号	WOS:000684596600001
WOS类目	Geography, Physical ; Management ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Physical Geography ; Business & Economics ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/370217
作者单位	[Adao, Telmo; Padua, Luis; Pinho, Tatiana M.; Sousa, Antonio; Sousa, Joaquim Joao; Morais, Raul; Peres, Emanuel] INESC Technol & Sci INESC TEC, Ctr Robot Ind & Intelligent Syst CRIIS, Porto, Portugal; [Adao, Telmo; Padua, Luis; Hruska, Jonas; Sousa, Antonio; Sousa, Joaquim Joao; Morais, Raul; Peres, Emanuel] Univ Tras Os Montes & Alto Douro, Sch Sci & Technol, Engn Dept, Vila Real, Portugal
推荐引用方式 GB/T 7714	Adao, Telmo,Padua, Luis,Pinho, Tatiana M.,et al. MULTI-PURPOSE CHESTNUT CLUSTERS DETECTION USING DEEP LEARNING: A PRELIMINARY APPROACH[C]:COPERNICUS GESELLSCHAFT MBH,2019:1-7.