Authors:
Fernando F. Putti
1
;
Luís Roberto Almeida Gabriel Filho
1
;
Camila Pires Cremasco
1
and
Antonio Evaldo Klar
2
Affiliations:
1
FCA, UNESP – Univ. Estadual Paulista, CET, UNESP – Univ. Estadual Paulista and Campus of Tupa, Brazil
;
2
CET, UNESP – Univ. Estadual Paulista and Campus of Tupa, Brazil
Keyword(s):
Growth, Water Stress, Foggy, Uncertain, Precision, Curves.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In the wake of the worldwide water supply crisis, several methods are being used to optimize the use of water,
mainly in agriculture, which is the main consuming factor. Magnetically treated water for agriculture is
beneficent due to an increase in quality and productivity. Current assay evaluates the effects of magnetically
treated water in lettuce cultivations throughout its cycle and determines the intermediate rates by fuzzy models
submitted at different reposition rates and assessed throughout the cycles. The assay was conducted in
randomized blocks with a 4 x 5 factor scheme, with 5 reposition laminas and 4 dates after transplant.
Development was evaluated by fuzzy mathematical modeling and by multiple polynomial regressions. Results
were compared with data collected on the field. The highest development occurred for treatments irrigated
with magnetically treated water, featuring a greater green aerial phytomass and number of leaves throughout
the cycle. The fuzzy model provide
d a more exact adjustment when compared with results from statistical
models.
(More)