Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

Computers and Electronics in Agriculture

Volumn 156, Issue , 2019, Pages 585-605

  Rehman, Tanzeel U ^a   Mahmud, Md Sultan ^b   Chang, Young K ^b   Jin, Jian ^a   Shin, Jaemyung ^b  

^a Purdue University   (United States)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Discriminant analysis; Fuzzy clustering; Gaussian mixture model; K means clustering; k Nearest Neighbour; Machine vision; Na ve Bayes; Statistical machine learning; Support vector machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; COST EFFECTIVENESS; CROPS; CULTIVATION; DISCRIMINANT ANALYSIS; FUZZY CLUSTERING; GAUSSIAN DISTRIBUTION; LEARNING SYSTEMS; MACHINERY; NEAREST NEIGHBOR SEARCH; SPECTRUM ANALYSIS; STATISTICS; SUPPORT VECTOR MACHINES;

GAUSSIAN MIXTURE MODEL; K - MEANS CLUSTERING; K-NEAREST NEIGHBOURS; MACHINE LEARNING APPLICATIONS; MACHINE LEARNING TECHNIQUES; MACHINE VISION SYSTEMS; STATISTICAL MACHINE LEARNING; SUPERVISED AND UNSUPERVISED LEARNING;

LEARNING ALGORITHMS;

ALGORITHM; CLUSTER ANALYSIS; CROP PRODUCTION; DISCRIMINANT ANALYSIS; FUTURE PROSPECT; FUZZY MATHEMATICS; GAUSSIAN METHOD; MACHINE LEARNING; NONDESTRUCTIVE TESTING; NUMERICAL MODEL; STATISTICAL ANALYSIS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINE;

EID: 85058223118     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.12.006     Document Type: Review

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