In-field cotton detection via region-based semantic image segmentation

Computers and Electronics in Agriculture

Volumn 127, Issue , 2016, Pages 475-486

  Li, Yanan ^a   Cao, Zhiguo ^a   Lu, Hao ^a   Xiao, Yang ^a   Zhu, Yanjun ^a   Cremers, Armin B ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Rheinische Friedrich Wilhelms Universität   (Germany)

Author keywords

Boll opening stage; Cotton; Image segmentation; Unsupervised region generation

Indexed keywords

AGRICULTURE; COTTON; CROPS; DECISION TREES; IMAGE PROCESSING; ITERATIVE METHODS; SEMANTICS;

COLOR AND TEXTURE FEATURES; DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE; ITERATIVE CLUSTERING; PRECISION AGRICULTURE; SEMANTIC IMAGE SEGMENTATIONS; STANDARD DEVIATION; UNSUPERVISED REGION GENERATION; WASSERSTEIN DISTANCE;

IMAGE SEGMENTATION;

COTTON; CROP YIELD; DATA SET; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PRECISION AGRICULTURE; SEGMENTATION; UNSUPERVISED CLASSIFICATION; YIELD RESPONSE;

GOSSYPIUM HIRSUTUM;

EID: 84978239436     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.07.006     Document Type: Article

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