Submitted:
06 November 2023
Posted:
07 November 2023
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Abstract
Keywords:
1. Introduction
1.1. Background on Soilless Vegetable Production
1.2. Importance of Next-Generation Technologies in Advancing the Field
1.3. Objective of the Review Article
2. Hydroponic Systems
2.1. Overview of Hydroponic Systems
2.2. Recent Technological Advancements in Hydroponics
2.3. Impact of Hydroponic Systems on Soilless Vegetable Production
2.4. Scalability and Replicability of Soilless Cultures
3. Substrate-Based Systems
3.1. Introduction to Substrate-Based Systems
3.2. Substrate Materials and Their Benefits
3.3. Innovations in Substrate-Based Cultivation Techniques
4. Automation and Precision Farming
4.1. Role of Automation in Soilless Vegetable Production
4.2. Application of Precision Farming/Agriculture Techniques in Hydroponics
4.3. Prospects and Trends in Automated Soilless Crop Systems
5. Sensing and Monitoring Technologies
5.1. Importance of Sensing and Monitoring in Soilless Vegetable Production
5.2. Advances in Sensor Technologies for Nutrient Management and Environmental Control
5.3. Real-Time Monitoring Systems for Optimizing Crop Growth and Resource Utilization
5.4. Case Studies Demonstrating the Effectiveness of Sensing and Monitoring Technologies
6. Artificial Intelligence and Data Analytics
6.1. Integration of Artificial Intelligence (AI) in Soilless Crop Systems
6.2. AI-Based Decision Support Systems for Optimizing Cultivation Parameters
6.3. Potential Challenges and Ethical Considerations in AI-Driven Cultivation
7. Environmental Sustainability and Resource Management
7.1. Role of Next-Generation Technologies in Enhancing Sustainability
7.2. Efficient Water and Nutrient Management Strategies
7.3. Energy-Saving Techniques in Soilless Vegetable Production
7.4. Life Cycle Assessment and Eco-Friendly Practices
8. Future Directions and Challenges
8.1. Promising Areas for Further Research and Development
8.2. Regulatory and Policy Considerations for Next-Generation Technologies
8.3. Potential Limitations and Hurdles to Widespread Adoption
9. Conclusions and Final Remarks
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Hernández-Martínez, N.R.; Blanchard, C.; Wells, D.; Salazar-Gutiérrez, M.R. Current State and Future Perspectives of Commercial Strawberry Production: A Review. Sci. Hortic. 2023, 312. [Google Scholar] [CrossRef]
- Atzori, G.; Mancuso, S.; Masi, E. Seawater Potential Use in Soilless Culture: A Review. Sci. Hortic. 2019, 249, 199–207. [Google Scholar] [CrossRef]
- Fussy, A.; Papenbrock, J. An Overview of Soil and Soilless Cultivation Techniques—Chances, Challenges and the Neglected Question of Sustainability. Plants 2022, 11. [Google Scholar] [CrossRef]
- Gonnella, M.; Renna, M. The Evolution of Soilless Systems towards Ecological Sustainability in the Perspective of a Circular Economy. Is. It Really the Opposite of Organic Agriculture? Agronomy 2021, 11. [Google Scholar] [CrossRef]
- Carrasco, G.; Fuentes-Penailillo, F.; Perez, R.; Rebolledo, P.; Manriquez, P. An Approach to a Vertical Farming Low-Cost to Reach Sustainable Vegetable Crops. In Proceedings of the 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022; Institute of Electrical and Electronics Engineers Inc., 2022. [Google Scholar]
- Fuentes-Penailillo, F.; Ortega-Farias, S.; Tian, F.; Perez, R.; Calderon, V.; Perez, D. Towards the Monitoring of Water Consumption of Crops Using Digital Agriculture Techniques. In Proceedings of the 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022; Institute of Electrical and Electronics Engineers Inc., 2022. [Google Scholar]
- Gutter, K.; Ortega-Farías, S.; Fuentes-Penailillo, F.; Moreno, M.; Vega-Ibánez, R.; Riveros-Burgos, C.; Albornoz, J. Estimation of Vineyard Water Status Using Infrared Thermometry Measured at Two Positions of the Canopy. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science, April 1 2022; 1335, pp. 331–337. [Google Scholar]
- Carrasco, G.; Manrıquez, P.; Galleguillos, F.; Fuentes-Peñailillo, F.; Urrestarazu, M. Evolution of Soilless Culture in Chile. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science, September 1 2021; 1321, pp. 267–273. [Google Scholar]
- Gumisiriza, M.S.; Ndakidemi, P.; Nalunga, A.; Mbega, E.R. Building Sustainable Societies through Vertical Soilless Farming: A Cost-Effectiveness Analysis on a Small-Scale Non-Greenhouse Hydroponic System. Sustain. Cities Soc. 2022, 83. [Google Scholar] [CrossRef]
- Hans-Peter Kläring Strategies to Control Water and Nutrient Supplies to Greenhouse Crops. A Review. Agronomie 2001, 21, 311–321. [CrossRef]
- Resh, H. Hydroponic Food Production A Definitive Guidebook for the Advanced Home Gardener and the Commercial Hydroponic Grower, 8th Edition. ed; CRC Press: Boca Raton, FL, 2022; ISBN 978-0-367-67822-7. [Google Scholar]
- Cowan, N.; Ferrier, L.; Spears, B.; Drewer, J.; Reay, D.; Skiba, U. CEA Systems: The Means to Achieve Future Food Security and Environmental Sustainability?Sustain. Food Syst. 2022, 6. [CrossRef]
- Ragaveena, S.; Shirly Edward, A.; Surendran, U. Smart Controlled Environment Agriculture Methods: A Holistic Review. Rev. Env. Sci. Biotechnol. 2021, 20, 887–913. [Google Scholar] [CrossRef]
- Hati, A.J.; Singh, R.R. AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset. IEEE Access 2023, 11, 35298–35314. [Google Scholar] [CrossRef]
- Gumisiriza, M.S.; Kabirizi, J.M.L.; Mugerwa, M.; Ndakidemi, P.A.; Mbega, E.R. Can Soilless Farming Feed Urban East Africa? An Assessment of the Benefits and Challenges of Hydroponics in Uganda and Tanzania. Environ. Chall. 2022, 6. [Google Scholar] [CrossRef]
- D’Amico, A.; De Boni, A.; Ottomano Palmisano, G.; Acciani, C.; Roma, R. Environmental Analysis of Soilless Tomato Production in a High-Tech Greenhouse. Clean. Environ. Syst. 2023, 11. [Google Scholar] [CrossRef]
- Wittmann, S.; Jüttner, I.; Mempel, H. Indoor Farming Marjoram Production—Quality, Resource Efficiency, and Potential of Application. Agronomy 2020, 10. [Google Scholar] [CrossRef]
- Gupta, M.; Abdelsalam, M.; Khorsandroo, S.; Mittal, S. Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access 2020, 8, 34564–34584. [Google Scholar] [CrossRef]
- Rotz, S.; Duncan, E.; Small, M.; Botschner, J.; Dara, R.; Mosby, I.; Reed, M.; Fraser, E.D.G. The Politics of Digital Agricultural Technologies: A Preliminary Review. Sociol. Rural. 2019, 59, 203–229. [Google Scholar] [CrossRef]
- Lajoie-O’Malley, A.; Bronson, K.; van der Burg, S.; Klerkx, L. The Future(s) of Digital Agriculture and Sustainable Food Systems: An Analysis of High-Level Policy Documents. Ecosyst. Serv. 2020, 45, 101183. [Google Scholar] [CrossRef]
- Windsor, G.W.; Schwarz, M. Soilless Culture for Horticultural Crop Production; FAO plant production and protection paper; 101; Food and Agriculture Organization of the United Nations: Rome, 1990; ISBN 9251029520. [Google Scholar]
- Szekely, I.; Jijakli, M.H. Bioponics as a Promising Approach to Sustainable Agriculture: A Review of the Main Methods for Producing Organic Nutrient Solution for Hydroponics. Water 2022, 14. [Google Scholar] [CrossRef]
- Bliedung, A.; Dockhorn, T.; Germer, J.; Mayerl, C.; Mohr, M. Experiences of Running a Hydroponic System in a Pilot Scale for Resource-Efficient Water Reuse. J. Water Reuse Desalination 2020, 10, 347–362. [Google Scholar] [CrossRef]
- Stegelmeier, A.A.; Rose, D.M.; Joris, B.R.; Glick, B.R. The Use of PGPB to Promote Plant Hydroponic Growth. Plants 2022, 11. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, N.T.; McInturf, S.A.; Mendoza-Cózatl, D.G. Hydroponics: A Versatile System to Study Nutrient Allocation and Plant Responses to Nutrient Availability and Exposure to Toxic Elements. Journal of Visualized Experiments 2016. [Google Scholar] [CrossRef]
- Sharma, N.; Acharya, S.; Kumar, K.; Singh, N.; Chaurasia, O.P. Hydroponics as an Advanced Technique for Vegetable Production: An Overview. J. Soil. Water Conserv. 2018, 17, 364. [Google Scholar] [CrossRef]
- Cooper, A. The ABC of NFT: Nutrient Film Technique; Grower Book, 1979.
- Nursyahid, A.; Setyawan, T.A.; Sa’diyah, K.; Wardihani, E.D.; Helmy, H.; Hasan, A. Analysis of Deep Water Culture (DWC) Hydroponic Nutrient Solution Level Control Systems. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1108, 012032. [Google Scholar] [CrossRef]
- Cai, J.; Veerappan, V.; Arildsen, K.; Sullivan, C.; Piechowicz, M.; Frugoli, J.; Dickstein, R. A Modified Aeroponic System for Growing Small-Seeded Legumes and Other Plants to Study Root Systems. Plant Methods 2023, 19. [Google Scholar] [CrossRef] [PubMed]
- Urrestarazu, M.; Carrasco Silva, G. Soiless Culture and Hydroponics; 2023; ISBN 978-84-8476-766-4. [Google Scholar]
- Quy, V.K.; Hau, N. Van; Anh, D. Van; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci. 2022, 12. [Google Scholar] [CrossRef]
- Alahi, M.E.E.; Sukkuea, A.; Tina, F.W.; Nag, A.; Kurdthongmee, W.; Suwannarat, K.; Mukhopadhyay, S.C. Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends. Sensors 2023, 23. [Google Scholar] [CrossRef] [PubMed]
- Monostori, I.; Heilmann, M.; Kocsy, G.; Rakszegi, M.; Ahres, M.; Altenbach, S.B.; Szalai, G.; Pál, M.; Toldi, D.; Simon-Sarkadi, L.; et al. LED Lighting – Modification of Growth, Metabolism, Yield and Flour Composition in Wheat by Spectral Quality and Intensity. Front. Plant Sci. 2018, 9. [Google Scholar] [CrossRef] [PubMed]
- Holzinger, A.; Keiblinger, K.; Holub, P.; Zatloukal, K.; Müller, H. AI for Life: Trends in Artificial Intelligence for Biotechnology. N. Biotechnol. 2023, 74, 16–24. [Google Scholar] [CrossRef] [PubMed]
- Kootstra, G.; Wang, X.; Blok, P.; Hemming, J.; van Henten, E. Selective Harvesting Robotics: Current Research, Trends, and Future Directions. Agriculture Robotics 2021, 95–104. [Google Scholar] [CrossRef]
- Clyde-Smith, D.; Campos, L.C. Engineering Hydroponic Systems for Sustainable Wastewater Treatment and Plant Growth. Appl. Sci. 2023, 13. [Google Scholar] [CrossRef]
- Aishwarya, J.M.; Vidhya, R. Study on the Efficiency of a Hydroponic Treatment for Removing Organic Loading from Wastewater and Its Application as a Nutrient for the “Amaranthus Campestris” Plant for Sustainability. Sustainability 2023, 15. [Google Scholar] [CrossRef]
- Tatas, K.; Al-Zoubi, A.; Christofides, N.; Zannettis, C.; Chrysostomou, M.; Panteli, S.; Antoniou, A. Reliable IoT-Based Monitoring and Control of Hydroponic Systems. Technologies 2022, 10. [Google Scholar] [CrossRef]
- Sneineh, A.A.; Shabaneh, A.A.A. Design of a Smart Hydroponics Monitoring System Using an ESP32 Microcontroller and the Internet of Things. MethodsX 2023, 102401. [Google Scholar] [CrossRef]
- Martinez-Mate, M.A.; Martin-Gorriz, B.; Martínez-Alvarez, V.; Soto-García, M.; Maestre-Valero, J.F. Hydroponic System and Desalinated Seawater as an Alternative Farm-Productive Proposal in Water Scarcity Areas: Energy and Greenhouse Gas Emissions Analysis of Lettuce Production in Southeast Spain. J. Clean. Prod. 2018, 172, 1298–1310. [Google Scholar] [CrossRef]
- Cifuentes-Torres, L.; Mendoza-Espinosa, L.G.; Correa-Reyes, G.; Daesslé, L.W. Hydroponics with Wastewater: A Review of Trends and Opportunities. Water Environ. J. 2021, 35, 166–180. [Google Scholar] [CrossRef]
- Parkes, M.G.; Azevedo, D.L.; Domingos, T.; Teixeira, R.F.M. Narratives and Benefits of Agricultural Technology in Urban Buildings: A Review. Atmosphere 2022, 13. [Google Scholar] [CrossRef]
- Lubna, F.A.; Lewus, D.C.; Shelford, T.J.; Both, A.J. What You May Not Realize about Vertical Farming. Horticulturae 2022, 8. [Google Scholar] [CrossRef]
- Velazquez-Gonzalez, R.S.; Garcia-Garcia, A.L.; Ventura-Zapata, E.; Barceinas-Sanchez, J.D.O.; Sosa-Savedra, J.C. A Review on Hydroponics and the Technologies Associated for Medium-and Small-Scale Operations. Agriculture 2022, 12. [Google Scholar] [CrossRef]
- Gruda, N.S. Increasing Sustainability of Growing Media Constituents and Stand-Alone Substrates in Soilless Culture Systems. Agronomy 2019, 9. [Google Scholar] [CrossRef]
- Sapkota, S.; Sapkota, S.; Liu, Z. Effects of Nutrient Composition and Lettuce Cultivar on Crop Production in Hydroponic Culture. Horticulturae 2019, 5. [Google Scholar] [CrossRef]
- Schmautz, Z.; Loeu, F.; Liebisch, F.; Graber, A.; Mathis, A.; Bulc, T.G.; Junge, R. Tomato Productivity and Quality in Aquaponics: Comparison of Three Hydroponic Methods. Water 2016, 8. [Google Scholar] [CrossRef]
- Yanti, C.W.B.; Dermawan, R.; Nafsi, N.S.; Rafiuddin, *!!! REPLACE !!!*; Bahrun, A.H.; Mollah, A.; Arafat, A. Response of Kale (Brassica Alboglabra L.) to Various Planting Media and Application of Liquid Inorganic Nutrition in DWC (Deep Water Culture) Hydroponic Systems. In Proceedings of the IOP Conference Series: Earth and Environmental Science; Institute of Physics Publishing, May 26 2020; 486. [Google Scholar]
- Tan, J.; Jiang, H.; Li, Y.; He, R.; Liu, K.; Chen, Y.; He, X.; Liu, X.; Liu, H. ; Growth, Phytochemicals, and Antioxidant Activity of Kale Grown under Different Nutrient-Solution Depths in Hydroponic. Horticulturae 2023, 9. [Google Scholar] [CrossRef]
- Lateef, A.; Nazir, R.; Jamil, N.; Alam, S.; Shah, R.; Khan, M.N.; Saleem, M. Synthesis and Characterization of Zeolite Based Nano-Composite: An Environment Friendly Slow Release Fertilizer. Microporous Mesoporous Mater. 2016, 232, 174–183. [Google Scholar] [CrossRef]
- Rombel, A.; Krasucka, P.; Oleszczuk, P. Sustainable Biochar-Based Soil Fertilizers and Amendments as a New Trend in Biochar Research. Science of the Total Environment 2022, 816. [Google Scholar] [CrossRef] [PubMed]
- Głąb, T.; Gondek, K.; Mierzwa–Hersztek, M. Biological Effects of Biochar and Zeolite Used for Remediation of Soil Contaminated with Toxic Heavy Metals. Sci. Rep. 2021, 11. [Google Scholar] [CrossRef] [PubMed]
- Singh Yadav, S.P.; Bhandari, S.; Bhatta, D.; Poudel, A.; Bhattarai, S.; Yadav, P.; Ghimire, N.; Paudel, P.; Paudel, P.; Shrestha, J.; et al. Biochar Application: A Sustainable Approach to Improve Soil Health. J. Agric. Food Res. 2023, 11. [Google Scholar] [CrossRef]
- Barrett, G.E.; Alexander, P.D.; Robinson, J.S.; Bragg, N.C. Achieving Environmentally Sustainable Growing Media for Soilless Plant Cultivation Systems – A Review. Sci. Hortic. 2016, 212, 220–234. [Google Scholar] [CrossRef]
- Pandey, K.; Singh, K.G.; Singh, A. Multi-Sensors Based Smart Nutrient Reuse Management System for Closed Soilless Culture under Protected Cultivation. Comput. Electron. Agric. 2023, 204. [Google Scholar] [CrossRef]
- Ojha, T.; Misra, S.; Raghuwanshi, N.S. Wireless Sensor Networks for Agriculture: The State-of-the-Art in Practice and Future Challenges. Comput. Electron. Agric. 2015, 118, 66–84. [Google Scholar] [CrossRef]
- Avgoustaki, D.D.; Xydis, G. How Energy Innovation in Indoor Vertical Farming Can Improve Food Security, Sustainability, and Food Safety? In Advances in Food Security and Sustainability; Elsevier Ltd., 2020; Vol. 5, pp. 1–51.
- Preite, L.; Solari, F.; Vignali, G. Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review. Sustainability 2023, 15. [Google Scholar] [CrossRef]
- Liang, Y.; Gao, G. Design and Analysis of Automatic Vegetable Harvesting Machine. In Proceedings of the Proceedings - 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2019; Institute of Electrical and Electronics Engineers Inc., November 1 2019; pp. 287–290.
- Tavan, M.; Wee, B.; Brodie, G.; Fuentes, S.; Pang, A.; Gupta, D. Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens. Front. Sustain. Food Syst. 2021, 4. [Google Scholar] [CrossRef]
- Montesano, F.F.; van Iersel, M.W.; Boari, F.; Cantore, V.; D’Amato, G.; Parente, A. Sensor-Based Irrigation Management of Soilless Basil Using a New Smart Irrigation System: Effects of Set-Point on Plant Physiological Responses and Crop Performance. Agric. Water Manag. 2018, 203, 20–29. [Google Scholar] [CrossRef]
- Steidle Neto, A.J.; Zolnier, S.; de Carvalho Lopes, D. Development and Evaluation of an Automated System for Fertigation Control in Soilless Tomato Production. Comput. Electron. Agric. 2014, 103, 17–25. [Google Scholar] [CrossRef]
- Dordas, C. Role of Nutrients in Controlling Plant Diseases in Sustainable Agriculture. A Review. Agron. Sustain. Dev. 2008, 28, 33–46. [Google Scholar] [CrossRef]
- Ikrang, E.G.; Ehiomogue, P.O.; Udoumoh, U.I. Hydroponics in Precision Agriculture: A Review; 2022;
- Herman, *!!! REPLACE !!!*; Surantha, N. Herman; Surantha, N. Intelligent Monitoring and Controlling System for Hydroponics Precision Agriculture.; IEEE, 2019.
- Chaiwongsai, J. Automatic Control and Management System for Tropical Hydroponic Cultivatio.; IEEE, 2019.
- Ferrández-Pastor, F.J.; García-Chamizo, J.M.; Nieto-Hidalgo, M.; Mora-Pascual, J.; Mora-Martínez, J. Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture. Sensors 2016, 16. [Google Scholar] [CrossRef] [PubMed]
- Kour, K.; Gupta, D.; Gupta, K.; Anand, D.; Elkamchouchi, D.H.; Pérez-Oleaga, C.M.; Ibrahim, M.; Goyal, N. Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors 2022, 22. [Google Scholar] [CrossRef]
- Sánchez Millán, F.; Ortiz, F.J.; Mestre Ortuño, T.C.; Frutos, A.; Martínez, V. Development of Smart Irrigation Equipment for Soilless Crops Based on the Current Most Representative Water-Demand Sensors. Sensors 2023, 23. [Google Scholar] [CrossRef] [PubMed]
- Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture 2022, 12. [Google Scholar] [CrossRef]
- Adeyemi, O.; Grove, I.; Peets, S.; Norton, T. Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation. Sustainability 2017, 9. [Google Scholar] [CrossRef]
- Sobri, N.A.; Ahmad, I.; Maharum, S.M.; Mansor, Z.; Rahman, A.H.A.; Aziz, A.A. Development of Hydroponics System and Data Monitoring Using Internet of Things. In Proceedings of the 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), September 2022; pp. 345–349. [Google Scholar]
- Naphtali, J.H.; Misra, S.; Wejin, J.; Agrawal, A.; Oluranti, J. An Intelligent Hydroponic Farm Monitoring System Using IoT. In Proceedings of the Data, Engineering and Applications; Sharma, S., Peng, S.-L., Agrawal, J., Shukla, R.K., Le, D.-N., Eds. Springer Nature Singapore: Singapore, 2022; pp. 409–420. [Google Scholar]
- Pandiselvam, R.; Sánchez Millán, F.; Ortiz, F.J.; Ortuño, T.C.M.; Frutos, A.; Martínez, V. Development of Smart Irrigation Equipment for Soilless Crops Based on the Current Most Representative Water-Demand Sensors. Sensors 2023, 23. [Google Scholar] [CrossRef]
- Christofi, A.; Margariti, G.; Salapatas, A.; Papageorgiou, G.; Zervas, P.; Karampiperis, P.; Koukourikos, A.; Tarantilis, P.A.; Kaparakou, E.H.; Misiakos, K.; et al. Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Photonic Sensors: A Preliminary Feasibility Study. 2023. [CrossRef]
- Popkova, E.G. Case Study of Smart Innovation in Agriculture on the Example of a Vertical Farm. In Smart Innovation in Agriculture; Popkova Elena, G., Sergi, B.S., Eds.; Springer Nature Singapore: Singapore, 2022; pp. 303–309. ISBN 978-981-16-7633-8. [Google Scholar]
- Kangogo, D.; Dentoni, D.; Bijman, J. Adopt. Clim. -Smart Agric. Among Smallhold. Farmers: Does Farmer. Entrep. Matter? Land. Use Policy 2021, 109, 105666. [CrossRef]
- Kaur Gaganjot and Upadhayaya, P. and C.P. The Study of Sensors in Soil-Less Farming Techniques for Modern Agriculture. In Proceedings of the Recent Advances in Intelligent Manufacturing; Kumar Harish and Jain, P.K. and G.S., Ed.; Springer Nature Singapore: Singapore, 2023; pp. 293–307.
- Eridani, D.; Wardhani, O.; Widianto, E.D. Designing and Implementing the Arduino-Based Nutrition Feeding Automation System of a Prototype Scaled Nutrient Film Technique (NFT) Hydroponics Using Total Dissolved Solids (TDS) Sensor. In Proceedings of the 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), October 2017; pp. 170–175. [Google Scholar]
- Kim, H.J.; Kim, W.K.; Roh, M.Y.; Kang, C.I.; Park, J.M.; Sudduth, K.A. Automated Sensing of Hydroponic Macronutrients Using a Computer-Controlled System with an Array of Ion-Selective Electrodes. Comput. Electron. Agric. 2013, 93, 46–54. [Google Scholar] [CrossRef]
- Erfianto, B.; Rakhmatsyah, A.; Ariyanto, E. Micro-Climate Control for Hydroponics in Greenhouses. In Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), June 2020; pp. 1–6. [Google Scholar]
- Siddiq, A.; Tariq, M.O.; Zehra, A.; Malik, S. ACHPA: A Sensor Based System for Automatic Environmental Control in Hydroponics. Food Sci. Technol. 2020, 40, 671–680. [Google Scholar] [CrossRef]
- Zhang, S.; Guo, Y.; Li, S.; Ke, Z.; Zhao, H.; Yang, J.; Wang, Y.; Li, D.; Wang, L.; Yang, W.; et al. Investigation on Environment Monitoring System for a Combination of Hydroponics and Aquaculture in Greenhouse. Inf. Process. Agric. 2022, 9, 123–134. [Google Scholar] [CrossRef]
- Ibayashi, H.; Kaneda, Y.; Imahara, J.; Oishi, N.; Kuroda, M.; Mineno, H. A Reliable Wireless Control System for Tomato Hydroponics. Sensors 2016, 16. [Google Scholar] [CrossRef] [PubMed]
- Mishra, P.; Jimmy, L.; Ogunmola, G.A.; Phu, T.V.; Jayanthiladevi, A.; Latchoumi, T.P. Hydroponics Cultivation Using Real Time Iot Measurement System. J. Phys. Conf. Ser. 2020, 1712, 012040. [Google Scholar] [CrossRef]
- Mapari, R.G.; Bhangale, K.B.; Patil, P.; Tiwari, H.; Khot, S.; Rane, S. An IoT Based Automated Hydroponics Farming and Real Time Crop Monitoring. In Proceedings of the 2022 2nd International Conference on Intelligent Technologies (CONIT); June 2022; pp. 1–5. [Google Scholar]
- Reyes-Yanes, A.; Martinez, P.; Ahmad, R. Real-Time Growth Rate and Fresh Weight Estimation for Little Gem Romaine Lettuce in Aquaponic Grow Beds. Comput. Electron. Agric. 2020, 179, 105827. [Google Scholar] [CrossRef]
- Yolanda, D.; Hindersah, H.; Hadiatna, F.; Triawan, M.A. Implementation of Real-Time Fuzzy Logic Control for NFT-Based Hydroponic System on Internet of Things Environment. In Proceedings of the 2016 6th International Conference on System Engineering and Technology (ICSET); October 2016; pp. 153–159. [Google Scholar]
- Ullah, A.; Aktar, S.; Sutar, N.; Kabir, R.; Hossain, A. Cost Effective Smart Hydroponic Monitoring and Controlling System Using IoT. Intell. Control Autom. 2019, 10, 142–154. [Google Scholar] [CrossRef]
- Marques Gonçalo and Aleixo, D. and P.R. Enhanced Hydroponic Agriculture Environmental Monitoring: An. Internet of Things Approach. In Proceedings of the Computational Science – ICCS 2019; Rodrigues João, M.F. and Cardoso, P.J.S. and M.J. and L.R. and K.V.V. and L.M.H. and D.J.J. and S.P.M.A., Ed.; Springer International Publishing: Cham, 2019; pp. 658–669.
- Bakhtar, N.; Chhabria, V.; Chougle, I.; Vidhrani, H.; Hande, R. An IoT Based Automated Hydroponics Farming and Real Time Crop Monitorin. In Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT); December 2018; pp. 205–209. [Google Scholar]
- Ramakrishnam Raju, S.V.S.; Dappuri, B.; Ravi Kiran Varma, P.; Yachamaneni, M.; Verghese, D.M.G.; Mishra, M.K. Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System. J. Nanomater. 2022, 2022, 4435591. [Google Scholar] [CrossRef]
- Andrianto, H.; Suhardi, *!!! REPLACE !!!*; Faizal, A. Development of Smart Greenhouse System for Hydroponic Agriculture. In Proceedings of the 2020 International Conference on Information Technology Systems and Innovation (ICITSI), October 2020; pp. 335–340. [Google Scholar]
- Dhal, S.B.; Mahanta, S.; Gumero, J.; O’Sullivan, N.; Soetan, M.; Louis, J.; Gadepally, K.C.; Mahanta, S.; Lusher, J.; Kalafatis, S. An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups. Sensors 2023, 23. [Google Scholar] [CrossRef] [PubMed]
- Mamatha, V.; Kavitha, J.C. Machine Learning Based Crop Growth Management in Greenhouse Environment Using Hydroponics Farming Techniques. Meas. Sens. 2023, 25, 100665. [Google Scholar] [CrossRef]
- Niswar, M. Design and Implementation of an Automated Indoor Hydroponic Farming System Based on the Internet of Things. Int. J. Comput. Digit. Syst. 2023, 14, 189–196. [Google Scholar] [CrossRef]
- Alotaibi, H.; Karsou, W.; Khan, S.; Tohmeh, S.; Bashar, A. Bustani: A Microcontroller-Based Automated Hydroponic Vertical Farming Solution. In Proceedings of the 2023 IEEE International Conference on Agrosystem Engineering, Technology & Applications (AGRETA), September 2023; pp. 56–61. [Google Scholar]
- Venkatraman, M.; Surendran, R. Design and Implementation of Smart Hydroponics Farming for Growing Lettuce Plantation under Nutrient Film Technology. In Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC); May 2023; pp. 1514–1521. [Google Scholar]
- Danush, G.; Sri, R.; Karthikeyan, A. Smart Hydroponics System for Soilless Farming Based on Internet of Things. In Proceedings of the Smart Technologies in Data Science and Communication; Ogudo Kingsley, A. and Saha, S.K. and B.D., Ed.. Springer Nature Singapore: Singapore, 2023; pp. 271–280. [Google Scholar]
- Thilakarathne, N.N.; Abu Bakar, M.S.; Abas, P.E.; Yassin, H. Towards Making the Fields Talks: A Real-Time Cloud Enabled IoT Crop Management Platform for Smart Agriculture. Front. Plant Sci. 2023, 13. [Google Scholar] [CrossRef] [PubMed]
- Albert, M.C.; Hans, H.; Karteja, H.; Widianto, M.H. Development of Hydroponic IoT-Based Monitoring System and Automatic Nutrition Control Using KNN. In Proceedings of the 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), February 2023; pp. 974–979. [Google Scholar]
- Wang, P. On Defining Artificial Intelligence. J. Artif. Gen. Intell. 2019, 10, 1–37. [Google Scholar] [CrossRef]
- Soori, M.; Arezoo, B.; Dastres, R. Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, a Review. Cogn. Robot. 2023, 3, 54–70. [Google Scholar] [CrossRef]
- Javaid, M.; Haleem, A.; Khan, I.H.; Suman, R. Understanding the Potential Applications of Artificial Intelligence in Agriculture Sector. Adv. Agrochem. 2023, 2, 15–30. [Google Scholar] [CrossRef]
- Sankaranarayanan, S. Applications of Artificial Intelligence for Smart Agriculture. In AI-Based Services for Smart Cities and Urban. Infrastructure; 2021; pp. 277–288.
- Panpatte Suraj and Ganeshkumar, C. Artificial Intelligence in Agriculture Sector: Case Study of Blue River Technology. In Proceedings of the Proceedings of the Second International Conference on Information Management and Machine Intelligence; Goyal Dinesh and Gupta, A.K. and P.V. and G.M. and P.M., Ed.. Springer Singapore: Singapore, 2021; pp. 147–153. [Google Scholar]
- Daoliang, L.; Chang, L. Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture. Smart Agric. 2020, 2, 1–20. [Google Scholar] [CrossRef]
- Sharma, S.; Verma, K.; Hardaha, P. Implementation of Artificial Intelligence in Agriculture. J. Comput. Cogn. Eng. 2022, 2, 155–162. [Google Scholar] [CrossRef]
- Bu, F.; Wang, X. A Smart Agriculture IoT System Based on Deep Reinforcement Learning. Future Gener. Comput. Syst. 2019, 99, 500–507. [Google Scholar] [CrossRef]
- Hemming, S.; de Zwart, F.; Elings, A.; Righini, I.; Petropoulou, A. Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors 2019, 19. [Google Scholar] [CrossRef] [PubMed]
- Wongchai, A.; Shukla, S.K.; Ahmed, M.A.; Sakthi, U.; Jagdish, M.; kumar, R. Artificial Intelligence - Enabled Soft Sensor and Internet of Things for Sustainable Agriculture Using Ensemble Deep Learning Architecture. Comput. Electr. Eng. 2022, 102, 108128. [Google Scholar] [CrossRef]
- Sachithra, V.; Subhashini, L.D.C.S. How Artificial Intelligence Uses to Achieve the Agriculture Sustainability: Systematic Review. Artif. Intell. Agric. 2023, 8, 46–59. [Google Scholar] [CrossRef]
- Jain, A.; Patel, H.; Nagalapatti, L.; Gupta, N.; Mehta, S.; Guttula, S.; Mujumdar, S.; Afzal, S.; Sharma Mittal, R.; Munigala, V. Overview and Importance of Data Quality for Machine Learning Tasks. In Proceedings of the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; Association for Computing Machinery: New York, NY, USA, 2020; pp. 3561–3562. [Google Scholar]
- Budach, L.; Feuerpfeil, M.; Ihde, N.; Nathansen, A.; Noack, N.; Patzlaff, H.; Naumann, F.; Harmouch, H. The Effects of Data Quality on Machine Learning Performance. 2022. [Google Scholar]
- Whang, S.E.; Roh, Y.; Song, H.; Lee, J.-G. Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective. VLDB J. 2023, 32, 791–813. [Google Scholar] [CrossRef]
- Whig, P. Leveraging AI for Sustainable Agriculture: Opportunities and Challenges. Transactions on Latest Trends in Artificial Intelligence 2023, 4. [Google Scholar]
- Rudrakar, S.; Rughani, P. IoT Based Agriculture (Ag-IoT): A Detailed Study on Architecture, Security and Forensics. Information Processing in Agriculture 2023. [CrossRef]
- Tzachor, A.; Devare, M.; King, B.; Avin, S.; Ó. hÉigeartaigh, S. Responsible Artificial Intelligence in Agriculture Requires Systemic Understanding of Risks and Externalities. Nat. Mach. Intell. 2022, 4, 104–109. [Google Scholar] [CrossRef]
- Racovita, M. Industry Briefing: Cybersecurity for the Internet of Things and Artificial Intelligence in the AgriTech Sector; London, UK: Industry Briefing PETRAS National Centre of Excellence for IoT~…: London, UK, 2021;
- Siregar, R.R.A.; Seminar, K.B.; Wahjuni, S.; Santosa, E. Vertical Farming Perspectives in Support of Precision Agriculture Using Artificial Intelligence: A Review. Computers 2022, 11, 135–153. [Google Scholar] [CrossRef]
- Bolandnazar, E.; Rohani, A.; Taki, M. Energy Consumption Forecasting in Agriculture by Artificial Intelligence and Mathematical Models. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2020, 42, 1618–1632. [Google Scholar] [CrossRef]
- Rodríguez, D.; Reca, J.; Martinez, J.; López-Luque, R.; Urrestarazu, M. Development of a New Control Algorithm for Automatic Irrigation Scheduling in Soilless Culture. Appl. Math. Inf. Sci. 2015, 9, 47–56. [Google Scholar] [CrossRef]
- Gayam, K.K.; Jain, A.; Gehlot, A.; Singh, R.; Akram, S.V.; Singh, A.; Anand, D.; Noya, I.D. Imperative Role of Automation and Wireless Technologies in Aquaponics Farming. Wirel. Commun. Mob. Comput. 2022, 2022, 8290255. [Google Scholar] [CrossRef]
- Tunalı, U.; Tüzel, I.H.; Tüzel, Y.; Şenol, Y. Estimation of Actual Crop Evapotranspiration Using Artificial Neural Networks in Tomato Grown in Closed Soilless Culture System. Agric. Water Manag. 2023, 284, 108331. [Google Scholar] [CrossRef]
- Kocian, A.; Carmassi, G.; Cela, F.; Chessa, S.; Milazzo, P.; Incrocci, L. IoT Based Dynamic Bayesian Prediction of Crop Evapotranspiration in Soilless Cultivations. Comput. Electron. Agric. 2023, 205, 107608. [Google Scholar] [CrossRef]
- Baille, M.; Baille, A.; Laury, J.C. A Simplified Model for Predicting Evapotranspiration Rate of Nine Ornamental Species vs. Climate Factors and Leaf Area. Sci. Hortic. 1994, 59, 217–232. [Google Scholar] [CrossRef]
- Othman, Y.; Bataineh, K.; Al-Ajlouni, M.; Alsmairat, N.; Ayad, J.; Shiyab, S.; Al-Qarallah, B.; St Hilaire, R. Soilless Agriculture, Highlighting Their Advantages, Potential Drawbacks, and Key Areas of Impact. Soilless Culture: Management of Growing Substrate, Water, Nutrient, Salinity, Microorganism and Product Quality. Fresenius Env. Bull. 2019, 28, 3249–3260. [Google Scholar]
- Montesano, F.F.; van Iersel, M.W.; Boari, F.; Cantore, V.; D’Amato, G.; Parente, A. Sensor-Based Irrigation Management of Soilless Basil Using a New Smart Irrigation System: Effects of Set-Point on Plant Physiological Responses and Crop Performance. Agric. Water Manag. 2018, 203, 20–29. [Google Scholar] [CrossRef]
- Elvanidi, A.; Katsoulas, N.; Ferentinos, K.P.; Bartzanas, T.; Kittas, C. Hyperspectral Machine Vision as a Tool for Water Stress Severity Assessment in Soilless Tomato Crop. Biosyst. Eng. 2018, 165, 25–35. [Google Scholar] [CrossRef]
- Vega-Ibáñez, R.; Ortega-FarÃías, S.; Fuentes-Peñailillo, F.; Gutter, K.; Albornoz, J. Estimation of Midday Stem Water Potential in Grapevine Leaves (‘Cabernet Sauvignon’) Using Spectral Reflectance Indices. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science (ISHS), Leuven, Belgium, February 6 2022; pp. 325–330. [Google Scholar]
- Gallardo, M.; Elia, A.; Thompson, R.B. Decision Support Systems and Models for Aiding Irrigation and Nutrient Management of Vegetable Crops. Agric. Water Manag. 2020, 240, 106209. [Google Scholar] [CrossRef]
- Kumari, S.; Pradhan, P.; Yadav, R.; Kumar, S. Hydroponic Techniques: A Soilless Cultivation in Agriculture. J. Pharmacogn. Phytochem. 2018, 7, 1886–1891. [Google Scholar]
- Tzortzakis, N.; Nicola, S.; Savvas, D.; Voogt, W. Editorial: Soilless Cultivation Through an Intensive Crop Production Scheme. Management Strategies, Challenges and Future Directions. Front. Plant Sci. 2020; 11. [Google Scholar] [CrossRef]
- Cho, W.J.; Kim, H.J.; Jung, D.H.; Kim, D.W.; Ahn, T.I.; Son, J.E. On-Site Ion Monitoring System for Precision Hydroponic Nutrient Management. Comput. Electron. Agric. 2018, 146, 51–58. [Google Scholar] [CrossRef]
- Massa, D.; Magán, J.J.; Montesano, F.F.; Tzortzakis, N. Minimizing Water and Nutrient Losses from Soilless Cropping in Southern Europe. Agric. Water Manag. 2020, 241, 106395. [Google Scholar] [CrossRef]
- Rouphael, Y.; Raimondi, G.; Caputo, R.; Pascale, S. De Fertigation Strategies for Improving Water Use Efficiency and Limiting Nutrient Loss in Soilless Hippeastrum Production. HortScience 2016, 51, 684–689. [Google Scholar] [CrossRef]
- Cámara-Zapata, J.M.; Brotons-Martínez, J.M.; Simón-Grao, S.; Martinez-Nicolás, J.J.; García-Sánchez, F. Cost–Benefit Analysis of Tomato in Soilless Culture Systems with Saline Water under Greenhouse Conditions. J. Sci. Food Agric. 2019, 99, 5842–5851. [Google Scholar] [CrossRef]
- Ghanayem, A.A.; Almohamed, S.; Al Assaf, A.; Majdalawi, M. Socioeconomic Analysis of Soil-Less Farming System -An Comparative Evidence from Jordan, The Middle East. Int. J. Food Agric. Econ. 2022, 10, 205–223. [Google Scholar] [CrossRef]
- Chen, D.; Zhao, H. Data Security and Privacy Protection Issues in Cloud Computing. In Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering; 2012; 1, pp. 647–651. [Google Scholar]
- Wang, F.; Harindintwali, J.D.; Yuan, Z.; Wang, M.; Wang, F.; Li, S.; Yin, Z.; Huang, L.; Fu, Y.; Li, L.; et al. Technologies and Perspectives for Achieving Carbon Neutrality. Innov. 2021, 2, 100180. [Google Scholar] [CrossRef] [PubMed]
- Manos, D.-P.; Xydis, G. A Multi-Criteria Linear Model on Carbon Footprint in Vertical Farms and Its Relation to Energy Demand and Operational Costs. Environ. Sci. Pollut. Res. 2022, 29, 79331–79342. [Google Scholar] [CrossRef] [PubMed]
- D’Amico, A.; De Boni, A.; Ottomano Palmisano, G.; Acciani, C.; Roma, R. Environmental Analysis of Soilless Tomato Production in a High-Tech Greenhouse. Clean. Environ. Syst. 2023, 11, 100137. [Google Scholar] [CrossRef]
- Golzar, F.; Heeren, N.; Hellweg, S.; Roshandel, R. Optimisation of Energy-Efficient Greenhouses Based on an Integrated Energy Demand-Yield Production Model. Biosyst. Eng. 2021, 202, 1–15. [Google Scholar] [CrossRef]
- Maraveas, C.; Karavas, C.-S.; Loukatos, D.; Bartzanas, T.; Arvanitis, K.G.; Symeonaki, E. Agricultural Greenhouses: Resource Management Technologies and Perspectives for Zero Greenhouse Gas Emissions. Agriculture 2023, 13. [Google Scholar] [CrossRef]
- Cuce, E.; Harjunowibowo, D.; Cuce, P.M. Renewable and Sustainable Energy Saving Strategies for Greenhouse Systems: A Comprehensive Review. Renew. Sustain. Energy Rev. 2016, 64, 34–59. [Google Scholar] [CrossRef]
- Muhammad, A.I.; Shitu, A.; Danhassan, U.A.; Kabir, M.H.; Tadda, M.A.; Lawal, A.M. Greenhouse Requirements for Soilless Crop Production: Challenges and Prospects for Plant Factories. In Next-Generation Greenhouses for Food Security; Shamshiri, R.R., Ed.; IntechOpen: Rijeka, 2021; p. Ch. 5. ISBN 978-1-83968-076-2. [Google Scholar]
- Karanisa, T.; Achour, Y.; Ouammi, A.; Sayadi, S. Smart Greenhouses as the Path towards Precision Agriculture in the Food-Energy and Water Nexus: Case Study of Qatar. Env. Syst. Decis. 2022, 42, 521–546. [Google Scholar] [CrossRef]
- Formolli, M.; Kleiven, T.; Lobaccaro, G. Assessing Solar Energy Accessibility at High Latitudes: A Systematic Review of Urban Spatial Domains, Metrics, and Parameters. Renew. Sustain. Energy Rev. 2023, 177, 113231. [Google Scholar] [CrossRef]
- Ahamed, M.S.; Guo, H.; Tanino, K. Energy Saving Techniques for Reducing the Heating Cost of Conventional Greenhouses. Biosyst. Eng. 2019, 178, 9–33. [Google Scholar] [CrossRef]
- Firdaus, N.; Samat, H.A.; Mohamad, N. Maintenance for Energy Efficiency: A Review. IOP Conf. Ser. Mater. Sci. Eng. 2019, 530. [Google Scholar] [CrossRef]
- Richa, A.; Touil, S.; Fizir, M.; Martinez, V. Recent Advances and Perspectives in the Treatment of Hydroponic Wastewater: A Review. Rev. Env. Sci. Biotechnol. 2020, 19, 945–966. [Google Scholar] [CrossRef]
- Putra, P.A.; Yuliando, H. Soilless Culture System to Support Water Use Efficiency and Product Quality: A Review. Agric. Agric. Sci. Procedia 2015, 3, 283–288. [Google Scholar] [CrossRef]
- Cucurachi, S.; Scherer, L.; Guinée, J.; Tukker, A. Life Cycle Assessment of Food Systems. One Earth 2019, 1, 292–297. [Google Scholar] [CrossRef]
- Toboso-Chavero, S.; Madrid-López, C.; Villalba, G.; Gabarrell Durany, X.; Hückstädt, A.B.; Finkbeiner, M.; Lehmann, A. Environmental and Social Life Cycle Assessment of Growing Media for Urban Rooftop Farming. Int. J. Life Cycle Assess. 2021, 26, 2085–2102. [Google Scholar] [CrossRef]
- Bonaguro, J.E.; Coletto, L.; Sambo, P.; Nicoletto, C.; Zanin, G. LCA Analysis of the Benefits Deriving from Sustainable Production Practices Applied to Cyclamen and Zonal Geranium; 2020. [Google Scholar] [CrossRef]
- Perrin, A.; Basset-Mens, C.; Gabrielle, B. Life Cycle Assessment of Vegetable Products: A Review Focusing on Cropping Systems Diversity and the Estimation of Field Emissions. Int. J. Life Cycle Assess. 2014, 19, 1247–1263. [Google Scholar] [CrossRef]
- Ilari, A.; Toscano, G.; Boakye-Yiadom, K.A.; Duca, D.; Foppa Pedretti, E. Life Cycle Assessment of Protected Strawberry Productions in Central Italy. Sustainability 2021, 13. [Google Scholar] [CrossRef]
- Maaoui, M.; Boukchina, R.; Hajjaji, N. Environmental Life Cycle Assessment of Mediterranean Tomato: Case Study of a Tunisian Soilless Geothermal Multi-Tunnel Greenhouse. Env. Dev. Sustain. 2021, 23, 1242–1263. [Google Scholar] [CrossRef]
- Gruda, N.S. Increasing Sustainability of Growing Media Constituents and Stand-Alone Substrates in Soilless Culture Systems. Agronomy 2019, 9. [Google Scholar] [CrossRef]
- Smith, M.J. Getting Value from Artificial Intelligence in Agriculture. Anim. Prod. Sci. 2020, 60, 46–54. [Google Scholar] [CrossRef]
- Sharma, R. Artificial Intelligence in Agriculture: A Review. In Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS); 2021; pp. 937–942. [Google Scholar]
- Eli-Chukwu, N.C. Applications of Artificial Intelligence in Agriculture: A Review. Engineering,Technology & Applied Science Research 2019, 9, 4377–4383. [Google Scholar]
- Sarkar, U.; Bannerjee, G.; Das, S.; Ghosh, I. Artificial Intelligence in Agriculture: A Literature Survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 2018, 7. [Google Scholar]
- Klerkx, L.; Rose, D. Dealing with the Game-Changing Technologies of Agriculture 4. 0: How Do We Manage Diversity and Responsibility in Food System Transition Pathways? Glob Food Sec 2020, 24, 100347. [Google Scholar] [CrossRef]
- Kos, D.; Kloppenburg, S. Digital Technologies, Hyper-Transparency and Smallholder Farmer Inclusion in Global Value Chains. Curr. Opin. Env. Sustain. 2019, 41, 56–63. [Google Scholar] [CrossRef]
- Regan, Á. ‘Smart Farming’ in Ireland: A Risk Perception Study with Key Governance Actors. NJAS - Wageningen Journal of Life Sciences, 2019; 90–91, 100292. [Google Scholar] [CrossRef]
- DeLonge, M.S.; Robbins, T.; Basche, A.D.; Haynes-Maslow, L. The State of Sustainable Agriculture and Agroecology Research and Impacts: A Survey of U. S. Scientists. J. Agric. Food Syst. Community Dev. 2020, 9, 159–184. [Google Scholar] [CrossRef]
- König, B.; Janker, J.; Reinhardt, T.; Villarroel, M.; Junge, R. Analysis of Aquaponics as an Emerging Technological Innovation System. J. Clean. Prod. 2018, 180, 232–243. [Google Scholar] [CrossRef]
- Gardezi, M.; Joshi, B.; Rizzo, D.M.; Ryan, M.; Prutzer, E.; Brugler, S.; Dadkhah, A. Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust. Agron. J. 2023, 1–12. [Google Scholar] [CrossRef]
- Goh, H.-H.; Vinuesa, R. Regulating Artificial intelligence Applications to achieve the Sustainable Development Goals. Discover Sustainability 2021, 2. [Google Scholar] [CrossRef] [PubMed]
- Fields, J.S.; Owen, J.; Lamm, A.; Altland, J.E.; Jackson, B.E.; Zheng, Y.; Oki, L.; Fontenot, K.; Samtani, J.; Campbell, B. Soilless Substrate Science: A North American Needs Assessment to Steer Soilless Substrate Research into the Future. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science (ISHS), Leuven, Belgium,, August 20 2021; pp. 313–318. [Google Scholar]
- Türkten, H.; Ceyhan, V. Environmental Efficiency in Greenhouse Tomato Production Using Soilless Farming Technology. J. Clean. Prod. 2023, 398, 136482. [Google Scholar] [CrossRef]
- Qian, C.; Murphy, S.I.; Orsi, R.H.; Wiedmann, M. How Can. AI Help. Improv. Food Saf. ? Annu. Rev. Food Sci. Technol. 2023, 14, 517–538. [CrossRef]
- Păvăloaia, V.-D.; Necula, S.-C. Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review. Electronics (Basel) 2023, 12. [Google Scholar] [CrossRef]
- Sun, W.; Zhang, Z.; Chen, Y.; Luan, F. Heterogeneous Effects of Robots on Employment in Agriculture, Industry, and Services Sectors. Technol. Soc. 2023, 75, 102371. [Google Scholar] [CrossRef]
- Mizik, T. How Can Precision Farming Work on a Small Scale? A Systematic Literature Review. Precis. Agric. 2023, 24, 384–406. [Google Scholar] [CrossRef]
- Marinello, F.; Zou, X.; Liu, Z.; Zhu, X.; Zhang, W.; Qian, Y.; Li, Y.; Karunathilake, E.M.B.M.; Le, A.T.; Heo, S.; et al. The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. 2023. [CrossRef]
- Barnes, A.P.; Soto, I.; Eory, V.; Beck, B.; Balafoutis, A.; Sánchez, B.; Vangeyte, J.; Fountas, S.; van der Wal, T.; Gómez-Barbero, M. Exploring the Adoption of Precision Agricultural Technologies: A Cross Regional Study of EU Farmers. Land. Use Policy 2019, 80, 163–174. [Google Scholar] [CrossRef]
- Ugur, M.; Mitra, A. Technology Adoption and Employment in Less Developed Countries: A Mixed-Method Systematic Review. World Dev. 2017, 96, 1–18. [Google Scholar] [CrossRef]
- Medici, M.; Pedersen, S.M.; Carli, G.; Tagliaventi, M.R. Environmental Benefits of Precision Agriculture Adoption. Econ. Agro-Aliment. 2020, 21, 637–656. [Google Scholar] [CrossRef]
- Banerjee, A.; Paul, K.; Varshney, A.; Nandru, R.; Badhwar, R.; Sapre, A.; Dasgupta, S. Chapter 8 - Soilless Indoor Smart Agriculture as an Emerging Enabler Technology for Food and Nutrition Security amidst Climate Change. In Plant Nutrition and Food Security in the Era of Climate Change; Kumar, V., Srivastava, A.K., Suprasanna, P., Eds.; Academic Press, 2022; pp. 179–225 ISBN 978-0-12-822916-3.
- Mitchell, S.; Weersink, A.; Bannon, N. Adoption Barriers for Precision Agriculture Technolo-gies in Canadian Crop Production. Can. J. Plant Sci. 2021, 101, 412–416. [Google Scholar] [CrossRef]
| Advanced Hydroponic Technology | Main Advantages | Main Disadvantages |
|---|---|---|
| AI-Based Monitoring Systems |
High precision in nutrient and pH detection, yield optimization | High cost, technical skills required for operation |
| Precision Agriculture Techniques | Efficient resource use, improved crop quality | High initial investment, complexity in implementation |
| Advanced Moisture and Nutrient Sensors | Real-time monitoring, improved irrigation efficiency | Installation and maintenance cost, potential technical failures |
| Automated Climate Control Systems |
Precise environmental control, improved crop quality and yield | High energy consumption, operational costs |
| Full-Spectrum LED Lighting |
Energy efficiency, improved plant growth | High initial cost, potential for plant stress if not managed correctly |
| Mobile Apps for Crop Management | Remote access for monitoring and control, ease of use | Connectivity dependency, feature limitations depending on the app |
| Substrate Materials | Main Advantages | Main Disadvantages |
|---|---|---|
| Coir | Renewable, excellent water retention, good aeration | Potential for high salt content, inconsistent quality |
| Perlite | Lightweight, good drainage, sterile | Expensive. Non-renewable, can float and cause uneven water distribution |
| Rockwool | Excellent water retention, sterile, easy to use | Non-biodegradable, manufacturing process has environmental impact |
| Vermiculite | High water retention, good nutrient-holding capacity |
Expensive. Non-renewable, potential for compaction over time |
| Expanded Clay Pebbles | Reusable, good drainage, lightweight |
High initial cost, potential for algae growth |
| Biochar | Renewable, improves soil structure, high nutrient retention | Variable quality, potential for high pH levels |
| Rice Hulls | Renewable, biodegradable, good aeration |
Potential for pest issues, decomposes over time |
| Real-Time Monitoring Systems | Advantages | Disadvantages | Key Metrics Monitored |
|---|---|---|---|
| Soil Moisture Sensors | Efficient water use, prevents overwatering |
Initial setup cost, maintenance |
Soil moisture levels |
| Nutrient Sensors | Optimizes nutrient delivery, reduces waste |
High cost, calibration required |
Nutrient concentration |
| pH Sensors | Maintains optimal pH levels, improves nutrient absorption |
Calibration needed, potential for errors |
pH levels |
| Temperature Sensors | Optimizes climate control, improves yield |
Energy consumption, cost | Air and soil temperature |
| Light Sensors | Efficient light use, improves photosynthesis |
Initial cost, limited to certain crops | Light intensity, spectrum |
| Humidity Sensors | Prevents mold, optimizes water use | Calibration required, maintenance |
Relative humidity |
| CO2 Sensors | Optimizes plant growth, improves yield |
High cost, complexity | CO2 concentration |
| AI Applications in Soilless Systems | Advantages | Disadvantages | Key Use-Cases |
|---|---|---|---|
| Predictive Analytics | Optimizes yield, reduces waste | High setup cost, data quality issues |
Yield prediction, disease detection |
| Machine Learning Algorithms |
Adaptive, improves over time | Complexity, requires expertise | Nutrient management, climate control |
| Computer Vision | Real-time monitoring, high accuracy |
Hardware cost, limited to certain crops |
Disease detection, growth monitoring |
| Natural Language Processing (NLP) |
User-friendly interfaces, easy monitoring |
Limited capabilities, language barriers | User interaction, data interpretation |
| Robotics and Automation | Labor-saving, high efficiency | High initial investment, maintenance |
Harvesting, planting, pruning |
| IoT Integration | Centralized control, real-time data |
Security risks, connectivity issues |
Sensor data aggregation, remote control |
| Next-Gen Technologies | Advantages for Sustainability | Potential Drawbacks | Key Areas of Impact |
|---|---|---|---|
| AI and Machine Learning |
Resource optimization, waste reduction |
Energy consumption, data privacy | Water and nutrient management |
| IoT Devices | Real-time monitoring, energy efficiency | Security risks, e-waste | Climate control, irrigation |
| Blockchain | Traceability, transparent supply chain |
Complexity, scalability issues | Food safety, environmental impact |
| Renewable Energy Sources | Low carbon footprint, long-term cost savings |
Initial setup cost, intermittency |
Energy supply for systems |
| Drones and Robotics | Reduced labor, precision agriculture |
High initial cost, regulatory hurdles | Planting, harvesting, monitoring |
| Limitations and Barriers | Impact on Adoption | Possible Solutions | Areas Affected |
|---|---|---|---|
| High Initial Cost | Barrier to entry for small-scale farmers | Government subsidies, financing options | Infrastructure, technology |
| Technical Complexity |
Steep learning curve, specialized skills required |
Training programs, user-friendly technology | System management, data analysis |
| Regulatory Uncertainty |
Compliance risks, lack of standardization | Development of industry standards, regulatory frameworks | Food safety, environmental impact |
| Energy Consumption |
Sustainability concerns, operational costs |
Renewable energy sources, energy-efficient systems |
Climate control, lighting |
| Water Quality | Risk of contamination, nutrient imbalances |
Water treatment systems, real-time monitoring | Nutrient delivery, plant health |
| Social Acceptance | Consumer skepticism, market adoption | Public awareness campaigns, transparent labeling |
Market penetration, consumer trust |
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