Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

Computers and Electronics in Agriculture

Volumn 155, Issue , 2018, Pages 157-166

  Su, Jinya ^a   Liu, Cunjia ^a   Coombes, Matthew ^a   Hu, Xiaoping ^b   Wang, Conghao ^b   Xu, Xiangming ^c   Li, Qingdong ^d   Guo, Lei ^d   Chen, Wen Hua ^a  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^b NORTHWEST A F UNIVERSITY   (China)

^c EAST MALLING RESEARCH   (United Kingdom)

^d BEIHANG UNIVERSITY   (China)

Author keywords

Multispectral image; Random forest; Spectral vegetation index (SVI); Unmanned Aerial Vehicle (UAV); Wheat yellow rust

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; CAMERAS; COSTS; CROPS; DECISION TREES; FARMS; LEARNING SYSTEMS; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

CLASSIFICATION PERFORMANCE; MACHINE LEARNING TECHNIQUES; MULTISPECTRAL IMAGES; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; SPECTRAL VEGETATION INDICES; SUSTAINABLE AGRICULTURE; YELLOW RUST;

AIRCRAFT DETECTION;

ACCURACY ASSESSMENT; AGRICULTURAL LAND; AIRBORNE SENSING; ALGORITHM; ALTERNATIVE AGRICULTURE; EXPERIMENTAL STUDY; MACHINE LEARNING; MONITORING SYSTEM; RUST DISEASE; SPECTRAL ANALYSIS; SPECTRAL RESOLUTION; UNMANNED VEHICLE; VEGETATION INDEX; WHEAT;

UNITED STATES;

PUCCINIA STRIIFORMIS F. SP. TRITICI; TRITICUM AESTIVUM;

EID: 85054927177     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.10.017     Document Type: Article

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