Abstract
The demand for fresh and healthy food has been increasing, and different options for growing sprouts have been presented to solve this, such as traditional techniques and cultivation under controlled conditions. However, sprout farming has not explored all the tools available to produce these foods under controlled conditions. This study presents an alternative to produce sesame seed sprouts in a micro-greenhouse applying intelligent control algorithms for vapor pressure deficit. There was an improvement of 56% in the germination percentage, 2.59 in the germination index, 9.7% in the production of proteins, 1.1% in ash and an increase of 77.03 mm in the sprouts’ length collected in the micro-greenhouse in comparison with the traditional technique. This was achieved by maintaining a mean error for soil moisture at 87% and 0.93 kPa for vapor pressure deficit by applying proportional–integral–derivative, fuzzy logic and neural network control algorithms in the micro-greenhouse. The study shows that the nutritional content, the measured germination parameters and the size are improved in sesame sprout production by applying intelligent control algorithms for vapor pressure deficit in a micro-greenhouse.
Funder
Tecnológico Nacional de México
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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