Yield prediction and growth mode characterization of greenhouse tomatoes with neural networks and fuzzy logic

Transactions of the ASABE

Volumn 52, Issue 6, 2009, Pages 2115-2128

  Fitz Rodriguez E ^a   Giacomelli, G A ^b  

^a UNIVERSITY OF ARIZONA   (United States)

^b University of Arizona   (United States)

Author keywords

Dynamic neural network; Fuzzy logic; Greenhouse tomato; Growth mode; Modeling; Pattern recognition; Reproductive; Vegetative

Indexed keywords

COMPUTER CIRCUITS; CONTROL EQUIPMENT; COPYRIGHTS; CROPS; CULTIVATION; DECISION MAKING; ENVIRONMENTAL MANAGEMENT; FRUITS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; GREENHOUSES; MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; PLANTS (BOTANY);

DYNAMIC NEURAL NETWORKS; GREENHOUSE TOMATOES; GROWTH MODES; REPRODUCTIVE; VEGETATIVE;

FUZZY LOGIC;

AGRICULTURAL MANAGEMENT; ARTIFICIAL NEURAL NETWORK; CROP YIELD; ENVIRONMENTAL CONDITIONS; FRUIT; FUZZY MATHEMATICS; GREENHOUSE ECOSYSTEM; GROWTH RATE; VEGETATIVE REPRODUCTION;

ARIZONA; TEXAS; TUCSON; UNITED STATES;

LYCOPERSICON ESCULENTUM;

EID: 76249099775     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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