Abstract
This paper contains Smart Hydroponic Control Scheme (HCS), which is used to monitor, control, and analyze the fault in hydroponics culture. The first part of the paper contains a brief literature about hydroponic systems and their utility. Then in next part, hydroponics systems and greenhouse development along with need of artificial intelligence (AI) and Internet of things (IoT)-based sensor technology for greenhouse culture of hydroponic are being highlighted. The last part of the paper comprises of controller block diagram and controlling approach. Hydroponic internal biological parameters are not directly measured in general. As in plants their conditions would be better predicted and monitored by their root-zone-micro-environment conditions. Therefore in hydroponics, we can control climate and harvest same plants round the year.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Y. Hashimoto, Recent strategies of optimal growth regulation by the speaking plant concept. Acta Hort. 260, 115–121 (1989)
J.C. Hoskins, K.M. Kaliyur, D.M. Himmelblau, Fault diagnosis in complex chemical plants using artificial neural networks. AIChE J. 37(1), 137–141 (1991)
E. Filho, A. de Carvalho, Evolutionary design of MLP neural network architectures, in Proceedings of the Fourth Brazilian Symposium on Neural Networks, pp. 58–65, Goiania, GO, Brazil, Dec 3–5 (1997)
K.P. Ferentinos, Artificial neural network modeling of pH and electrical conductivity of hydroponic systems, MS Thesis, Cornell University Libraries, Ithaca, NY (1999)
K.P. Ferentinos, L.D. Albright, B. Selman, Neural network based detection of mechanical, sensor and biological faults in deep-troughh ydroponics. Comput. Electron. Agric. Spec. Issue Artif. Intell. Agric. (2002)
K.P. Ferentinos, Neural network fault detection and diagnosis in deep-trough hydroponic systems. Ph.D. Dissertation, Cornell University Libraries, Ithaca, NY (2002)
A.W. Al-Kayssi, Spatial variability of soil temperature under Greenhouse conditions. Renew. Energy 27, 453–462 (2002)
H. Sundmaeker, P. Guillemin, P. Friess, S. Woelfflé (eds.), Publications Office of the European Union, Luxembourg (2010)
G.L. Atzori, A. Iera, G. Morabito, The Internet of things: a survey computer network. Comput. Netw. 54, 2787–2805 (2010)
H.S. Grewala, B. Maheshwaria, S.E. Parksb, Water and nutrient use efficiency of a low-cost hydroponic greenhouse for acucumber crop: An Australian case study. Agric. Water Manag. 98, 841–846 (2011)
I. Mohanraj, Field, “monitoring and automation using IoT in agriculture domain”. Procedia Comput. Sci. 93, 931–939 (2016)
M. Azaza, C. Tanougast, E.Fabrizio, A. Mami, Smart greenhouse fuzzy logic based control system en hanced with wireless data monitoring, vol. 61, pp. 297–307 (2016)
J. delSagrado, J.A. Sánchez, F. Rodríguez, M. Berenguel, Networks for greenhouse temperature control. J. Appl. Logic 17, (25–35) 2016
O. Długosz-Grochowska, A. Kołton, R. Wojciechowska, Modifying folate and polyphenol concentrations in Lamb’s lettuce by the use of LED supplemental lighting during cultivation in greenhouses. J. Funct. Foods 26, 228–237 (2016)
Libelium, 50 Sensor applications for a smarter world, http://www.libelium.com/top_50iot_sensor_applications_ranking
A.S.D. Rai, R. Pawar, D. Sharma, S. Sen, S.K. Gupta, Algorithms for synchrophasor enabled digital relay in differential protection scheme, in Proceedings of International Conference on Recent Advancement on Computer and Communication, Lecture Notes in Networks and Systems vol. 34. Springer Nature Singapore Pte Ltd., https://doi.Org/10.1007/978-981-10-8198-9_4. Book Id: 448040_1_EN, Book ISBN: 978-981-10-8197-22018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rai, A.S.D., Pawar, R., Pandey, A., Rajeshwari, C.S., Gwal, A.K. (2021). Smart Artificial Intelligent-Based Controller for Hydroponic: New Technique for Soilless Plantation. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-7106-0_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7105-3
Online ISBN: 978-981-15-7106-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)