scholarly journals Control of Vapor Pressure Deficit (VPD) on Black Sesame Seed (Sesamum indicum L) Sprout Production in a Micro-Greenhouse Using Intelligent Control

2021 ◽  
Vol 11 (17) ◽  
pp. 7957
Author(s):  
Luis E. Barreto-Salazar ◽  
Jesús J. Rochín-Medina ◽  
Julio C. Picos-Ponce ◽  
David E. Castro-Palazuelos ◽  
Guillermo J. Rubio-Astorga
Keyword(s):  
Vapor Pressure ◽  
Intelligent Control ◽  
Vapor Pressure Deficit ◽  
Sesame Seed ◽  
Germination Percentage ◽  
Network Control ◽  
Control Algorithms ◽  
Neural Network Control ◽  
Controlled Conditions ◽  
Germination Parameters

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.

The Summary of Tunnel Boring Machine Control Based on the Neural Network

2013 ◽  
Vol 706-708 ◽  
pp. 1090-1093
Author(s):  
Jin Zhi Liu ◽  
Jun Lin ◽  
Li Ming Ma ◽  
Shen Shan Li
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Tunnel Boring Machine ◽  
Network Control ◽  
Neural Network Control ◽  
Tunnel Boring ◽  
Process Uncertainty ◽  
The Neural Network ◽  
Machine Control ◽  
The Ideal

Conventional control of tunnel boring machine can't achieve the ideal effect because of the complexity of its work process, uncertainty of construction environment. In order to solve the question, the intelligent control is expected. The neural network control has many characteristics such as self-adaption, self-organization, and can modify the corresponding parameters by learning about external knowledge, so it becomes research focus of tunnel boring machine control system in recent years. In this paper the advantage and disadvantage of several kinds of neural network used in the tunnel boring machine control are introduced, the development trends of tunnel boring machine control is predicted.

Application of Fuzzy Neural Network in the Intelligent Control of Biomass Gasifier

2014 ◽  
Vol 1044-1045 ◽  
pp. 712-715
Author(s):  
Qin Hui Gong
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Flue Gas ◽  
Fuzzy Neural Network ◽  
Air Flow ◽  
Flow Simulation ◽  
Network Control ◽  
Neural Network Control ◽  
Control Effect ◽  
Fuzzy Neural

In view of biomass gasify process which has nonlinear, non-minimum- phase, big delay and strong load interference characteristics, a fuzzy neural network control algorithm for biomass gasifier is presented. Gasified process of biomass gasifier has been researched, and intelligent control of temperature and primary air flow for biomass gasifier has been designed. The control objects are gasifier temperature and flue gas oxygen content , the regulation objects are the material feed flow and primary air flow. Simulation results show that, this system has better control effect than the traditional fuzzy control system.

Intelligent Control on Valve-Controlled Motor Speed Servo System

2014 ◽  
Vol 945-949 ◽  
pp. 1543-1546
Author(s):  
Shou Yong Jiang ◽  
Yan Jun Liu
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Motor System ◽  
Control Method ◽  
Network Control ◽  
Neural Network Control ◽  
Motor Speed ◽  
Fuzzy Neural ◽  
Adaptive Ability ◽  
Hydraulic Valve

A intelligent speed control method for the hydraulic valve-controlled motor system was presented based on the fuzzy-neural network control which introduces the fuzzy control into neural network. The simulation results showed the self-adaptive ability and controlling performance of the hydraulic valve-controlled motor system was improved.

Effect of Mycorrhizal Fungi on Gas Exchange of Micropropagated Guava Plantlets (Psidium guajava L.) during Acclimatization and Plant Establishment

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 501c-501
Author(s):  
Andrés A. Estrada-Luna ◽  
Jonathan N. Egilla ◽  
Fred T. Davies
Keyword(s):  
Tissue Culture ◽  
Stomatal Conductance ◽  
Gas Exchange ◽  
Vapor Pressure ◽  
Mycorrhizal Fungi ◽  
Vapor Pressure Deficit ◽  
Psidium Guajava ◽  
Glomus Etunicatum ◽  
Plant Establishment ◽  
Use Efficiency

The effect of mycorrhizal fungi on gas exchange of micropropagated guava plantlets (Psidium guajava L.) during acclimatization and plant establishment was determined. Guava plantlets (Psidium guajava L. cv. `Media China') were asexually propagated through tissue culture and acclimatized in a glasshouse for eighteen weeks. Half of the plantlets were inoculated with ZAC-19, which is a mixed isolate containing Glomus etunicatum and an unknown Glomus spp. Plantlets were fertilized with modified Long Ashton nutrient solution containing 11 (g P/ml. Gas exchange measurements included photosynthetic rate (A), stomatal conductance (gs), internal CO2 concentration (Ci), transpiration rate (E), water use efficiency (WUE), and vapor pressure deficit (VPD). Measurements were taken at 2, 4, 8 and 18 weeks after inoculation using a LI-6200 portable photosynthesis system (LI-COR Inc. Lincoln, Neb., USA). Two weeks after inoculation, noninoculated plantlets had greater A compared to mycorrhizal plantlets. However, 4 and 8 weeks after inoculation, mycorrhizal plantlets had greater A, gs, Ci and WUE. At the end of the experiment gas exchange was comparable between noninoculated and mycorrhizal plantlets.

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