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This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0.
Axel Escamilla-García; Genaro M. Soto-Zarazúa; Manuel Toledano-Ayala; Edgar Rivas-Araiza; Abraham Gastélum-Barrios. Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. Applied Sciences 2020, 10, 3835 .
AMA StyleAxel Escamilla-García, Genaro M. Soto-Zarazúa, Manuel Toledano-Ayala, Edgar Rivas-Araiza, Abraham Gastélum-Barrios. Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. Applied Sciences. 2020; 10 (11):3835.
Chicago/Turabian StyleAxel Escamilla-García; Genaro M. Soto-Zarazúa; Manuel Toledano-Ayala; Edgar Rivas-Araiza; Abraham Gastélum-Barrios. 2020. "Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development." Applied Sciences 10, no. 11: 3835.
Aquaculture has been supporting human demands for fish products for centuries and is an important industry worldwide. Global production from aquaculture has been increasing steadily, having more than doubled in the last decade; aquaculture now supplies one-third of seafood consumed worldwide. However, the expansion of aquaculture has been accompanied by degradation of the natural environment, especially on marine aquaculture. Direct impacts of fisheries and aquaculture are habitat modification, collection of wild seedstock, changes of food webs, introduction of nonnative fish species, and diseases that harm wild fish populations, and nutrient pollution. According to the FAO, major issues that need to be addressed are problems with access to proper technology and financial resources, together with environmental impacts and diseases. Some others argue that further increases in aquaculture production will come mainly from further investment in biotechnology or nanotechnology ranging from protein expression and DNA vaccines, water filtration and remediation, nanoparticles, gene delivery (and chips) to transgenic technologies. The purpose of this chapter is to present the progress in the research about to get a sustainable aquaculture.
Genaro M. Soto-Zarazúa; J. Fernando García-Trejo; Manuel Toledano-Ayala; Edgar Rivas-Araiza. Aquatic Biosystems: Applications in Aquacultural Engineering as a Sustainable Technology. Biosystems Engineering: Biofactories for Food Production in the Century XXI 2014, 277 -287.
AMA StyleGenaro M. Soto-Zarazúa, J. Fernando García-Trejo, Manuel Toledano-Ayala, Edgar Rivas-Araiza. Aquatic Biosystems: Applications in Aquacultural Engineering as a Sustainable Technology. Biosystems Engineering: Biofactories for Food Production in the Century XXI. 2014; ():277-287.
Chicago/Turabian StyleGenaro M. Soto-Zarazúa; J. Fernando García-Trejo; Manuel Toledano-Ayala; Edgar Rivas-Araiza. 2014. "Aquatic Biosystems: Applications in Aquacultural Engineering as a Sustainable Technology." Biosystems Engineering: Biofactories for Food Production in the Century XXI , no. : 277-287.