: Horticulture is the science, technology, and business involved in intensive plant cultivation for human use. It is practiced from the individual level in a garden up to the activities of a multinational corporation. Horticulture is very often described as both science and art [2, 3


]. Hydroculture: Hydroculture is the growing of plants in soilless medium, or an aquatic based environment. All the plant nutrients are distributed via water. Hydroponics: Hydroponics is a subset of hydroculture and is a method of growing plants using mineral nutrient solutions,

Aeroponic Greenhouse as an Autonomous System Using Intelligent Space for Agriculture Robotics

Martin Pala, Ladislav Mizenko, Marian Mach, Tyler Reed


in water, without soil. Terrestrial plants may be grown with their roots in the mineral nutrient solution only (liquid hydroponic systems) or in an inert medium, such as perlite, mineral wool, gravel, expanded clay pebbles or coconut husk (aggregate hydroponic systems). Hydroponics


is a subset of soilless culture, but many types of soilless culture do not use the mineral nutrient solutions required for hydroponics. Aeroponics: Aeroponics on the other hand is considered to be another form of hydroponic technique as water is used to transmit nutrients


as well. Aeroponics is the process of growing plants in an air or mist environment without the use of soil or an aggregate medium [4]. According to AgriHouse (product outcome of NASA research program), growers choosing to employ the aeroponics method can reduce water usage by 98 percent, fe


rtilizer usage by 60 percent, and pesticide usage by 100 percent, all while maximizing their crop yields by 45 to 75 percent [4]. 2.1 Hydroponics vs. Aeroponics Hydroponics and aeroponics are both highly efficient methods of growing plants without them ever touching any soil. Both p


erform very well indoors or outdoors, are easy to maintain and relatively easy to automate, so during the plant growing process less attention from humans is needed. In addition the following advantages are being considered for both aeroponics and hydroponics systems [4, 5, 6, 7, and 8]: • No


soil is needed, no nutrition pollution • The water stays in the system, so can be reused • Lower nutrition requirements, nutrition control possibility • Stable and high yields, healthier plants as no pests are used • Energy efficient, easy harvesting Despite their many similaritie


s (and the fact that aeroponics is actually a type of hydroponics), aeroponics and hydroponics techniques also have some important differences. The most distinctive characteristic is that aeroponics uses no growing medium at all, while hydroponics uses growing medium.


Also the distribution of nutrients is different. Using hydroponics systems, plants may be suspended in the water full-time or the nutrients can be distributed by a continuous or even an intermittent flow. Aeroponic plants are never placed into water, even for a minute. Instead, aeroponic p


lants receive nutrients from a nutrient-rich water solution that is sprayed onto their dangling roots and lower stem several times an hour. The main advantage of nutrient delivery using aeroponics systems is that the plants are kept in a relatively closed e


nvironment, so diseases are not spread rapidly, while in traditional hydroponic methods plant diseases can be spread via nutrient distribution system from plant to plant. Another advantage of aeroponics is that suspended aeroponic plants receive 100% of the available oxygen and carbon diox


ide to the roots zone, stems, and leaves, thus accelerating growth and reducing rooting times [stoner]. According to the NASA research aeroponics requires 65% less water than hydroponics approach and aeroponically grown plants requires ¼ the nutrient input compared to hydropon


ically grown plants [4, 9]. The main problem of both hydroponics and especially aeroponics is that without soil as some kind of water and nutrient buffer, any failure of the aeroponic or hydroponic system leads to rapid death of grown plant. For this reason, more sophisticated


methods have to be used for fault detection, real-time monitoring, control and automation of such systems. Utilization of artificial intelligence in hydroponic and aeroponic systems may lead not only to early fault detection, thus avoiding damage to grown plants, but may al


so help to fully automate all the processes required in aeroponics and hydroponics and adapt to current needs of grown plants in real-time without any or small interventions of human operators, help to lower costs and make the whole process more efficient and likely more profitable. In the follo


wing section we provide a preview of some advanced methods and techniques currently applied in real life solutions or currently being developed or considered for real application. 2.2 Current Trends and Related Work As previously mentioned, hydroponic and aeroponic systems have subs


tantial vulnerability which can increase the complexity of their successful unattended application in real production. Problems like the failure of water pumps, nutrient distribution and preparation, nozzle clogging and many others require special attention to avoid damage or rapi


d death of growing plants. In this chapter we provide a basic preview of current trends and selected related work to hydroponics and aeroponics done so far. For example, the optimization of long-term plant growth in hydroponics, a hierarchical intelligent control system consistin


g of an expert system and a hybrid system based on genetic algorithms and neural networks was proposed in [10]. These two control systems were used appropriately, depending on the plant growth. Using this approach, the plant growth is controlled by the nutrient concentra


tion of the solution. The expert system was used for determining the appropriate set-points of nutrient concentration through the whole of the growth stages, and the hybrid system for determining the optimal set-points of nutrient concentration which maximize total leaf lengt


h (TLL) and stem diameter (SD) during the initial growth (seedling) stage. In the hybrid system, TLL/SD as affected by nutrient concentration was first identified using neural networks and then the optimal value was determined through simulation of the identified model using genetic algorithms. T


he set-points from the expert system were similar to those used by a skilled grower. Further details and experimental results can be found in [10]. Early disease detection is crucial especially when using hydroponic configuration as the disease may spread quickly via the nutrient distribu


tion system. JAPIEST, an integral intelligent system for the diagnosis and control of tomatoes diseases and pests in hydroponic greenhouses was developed [11]. JAPIEST is focused on the prevention, diagnosis and control of diseases that affect tomatoes and is capable of


early detection of candidate diseases and suitable control treatment proposals. Another recent work deals with fault detection using artificial intelligence methods. Neural-network systems capable of detecting of mechanical and biological faults in deep-trough hydroponics are b


ased on utilization of two separate fault detection systems. The first fault detection system is based on detection of faulty operation using sensor information, as inputs for neural network (pH, EC, air temperature, nutrient solution temperature, humidity, light intensity). T


he second fault detection system is focused on the detection of a category of biological faults (transpiration rate). The proposed neural-network model was able to detect mechanical problems using the first approach in most of the cases within 20 to 40 minutes, which may be sufficien


t for both hydroponics and aeroponics configurations. In the case of the biological fault detection, neural network model generally detected faults within 2-3 hours, where the output was either 0 – normal, or 1 – faulty operation [12, 13]. All the previously described growth optimizat


ion methods or disease and fault detection systems require input information which can be easily acquired by installing suitable sensors. The recent publication “Monitoring of an Aeroponic Greenhouse with Sensor Network” describes a possible design and implementation of a wirele


ss sensor network for greenhouse environment monitoring [14]. The real-time information obtained from the sensor nodes may be utilized to optimize temperature, ventilation, artificial lighting and nutrient solution properties control. More recent work about the real time gre


enhouse monitoring can be found in [15, 16 and 17] The following subchapter is dedicated to a robust system architecture that aims to utilize all the available methods to achieve rapid power optimization, plant growth, early fault detection and the highest possible automation level.


2.3 HAPI The Hydroponic Automation Platform Initiative (HAPI) develops and distributes control modules for automated food production systems, including and especially hydroponic and aeroponic systems. Through the initiative, a steady line of monitoring and control opti


ons are being evolved and optimized. Control options include control of lighting, water pumps, nutrient and pH dosing pumps, automated shades and louvers, heaters, air coolers, water chillers, humidifiers and dehumidifiers and security mechanisms. Support is also being developed


for a wide array of sensors including temperature, humidity, moisture of growth mediums, water levels, light intensity and color, pH, electrical conductivity and weather-related information. Sensing can be performed in plant, section, zone and site-wide formats. Artifacts from t


his aspect of the project include: hardware design, schematic drawings, firmware, management software, reporting functions, structural designs and process documentation. HAPI is not solely concerned with automation. The initiative includes several programs that together will serv


e to achieve the primary goal of increasing high-yield hydroponic production of clean food in urban settings. A core value of the initiative is “clean” food. In this case, clean means as close to organic as possible and without genetic modifications. One challenge in this re


gard is the diminishing supply of nongmo seeds. To this end, HAPI will initiate a worldwide network of “clean seed” production and distribution points. The primary goal of this program is to establish a global distribution web, by collaborating with similar programs with l


ocal focus, so that local access to a worldwide supply of diverse clean seeds is provided to as many urban farmers as possible. By providing automation modules and structural designs, a clean seed network and a best practice application, HAPI will dramatically lower the barriers to high-yiel


d, sustainable food production to individuals and communities across the planet. HAPI is an open title project with many collaborators. The research project of aeroponic greenhouse proposed in this paper will share a lot of features and is planned to be fully compatible with al


l HAPI elements. 3 Intelligent Space Intelligent space (iSpace) is an area or room that monitors what is happening in it, can make decisions and react according to these decisions [18]. Monitoring is performed by using sensors distributed in intelligent space, which may include cameras, mi


crophones, temperature and humidity sensors, ultrasonic and other range finding sensors. Actuators on the other hand provide information for inhabitants of iSpace using monitors or speakers, or can execute actions through robotic devices to manipulate with the real world objects. Since Has


himoto Laboratory proposed Intelligent Space in 1996, research has started in many different fields from robotics and mobile robot positioning and navigation [19] to health care [20], intelligent vehicles, transportation spaces and traffic control [21], intelligent homes and


rooms [22] or service robotics in large-scale dynamic environments [23]. Various research disciplines are involving into intelligent space, while it incorporates wide scope of issues needed to be solved. Most of the research is done in following scope: smart cameras


and devices, flexibility and scalability of system architecture and networking, signal processing and inferences, and real-time execution of actions. Typical high-level architecture of intelligent space consists of a communication layer connecting all devices, sensing, an acti


ng layer for collecting data and providing feedback by performing actions to physical world, and a processing layer responsible for handling and storing data and making decisions. In an effort to achieve the intelligent exchange of information, it is necessary to build up pervasiv


e communication infrastructure. Substantial research in this area has focused on various aspects of network communication. Recent studies focus on wireless networking with a goal of creating a communications layer for the fast and reliable transfer of information and knowledge. ISpace is cha


racterized by specific properties. One important aspect is modularity. It is necessary to have the ability to add and remove components in real-time without system downtime. In order to use iSpace in a wide variety of environments, all of the component systems must be scalable. As mentioned,


iSpace consists of devices like sensors, actuators and other components spatially distributed in the environment. All these devices are connected into the network, which creates a space for the emergence of difficulties to solve. Greenhouses provide ideal environments for the applica


tion of intelligent space methods. Covering a greenhouse complex with networked sensors and actuators can have a strong impact on the entire automation process and fundamentally transforms the greenhouse to a “robot ready” environment. This opens paths to not only monitoring and fault d


iagnostics, but also to robotic crop collection and many other robotic applications. 4 Aeroponics Control System Architecture Proposal We propose the control architecture for remote operation, monitoring, diagnostics and automation of the processes realized during the standard


operation of aeroponic or hydroponic greenhouse configuration. Our research is specialized for aeroponic configuration, but is fully compatible with the approach adopted by the previously described HAPI project. The main reason of this compatibility is further cooperation and ac


celeration of technology application. The following picture describes the main architecture of control and communication mechanisms between a remote human or virtual operator (e.g. expert system for autonomous plant growing) and an aeroponic or hydroponic greenhouse. Fig. 1. The


proposed architecture of control and communication mechanism between human/virtual operator and aeroponic/hydroponic system. The greenhouse is equipped with its own sensor network and actuator network for climate monitoring and adjustment and basically creates the


intelligent space and robot-ready environment. Every module is connected to the main controller which provides the communication with “Aero Pots” and remote modules (top of the picture). Main controller is capable of independent control or might be dependent


on control signals from a remote human or virtual operator. A virtual operator consists of three important modules: Communication module, module for monitoring, fault diagnostics remote control and optimization and database module. Interaction with a human operator is provided using a huma


n machine interface via web interface, client application or iOS/Android application on a smartphone. During the first stage of the project optimization will be aimed to lower power consumption of aeroponic system preserving maximum possible growth of the plants using various methods of artific


ial intelligence (e.g. genetic algorithms, adaptive fuzzy cognitive maps, etc…). Aero Pot is working codename for a highly scalable aeroponic system designed in our department. The Aero Pot prototype is visualized on the following picture. Fig. 2. CAD design of Aero Pot prototyp


e, visualizing nozzle installation and process of nutrient distribution to the roots of plants. The finishing of nutrition distribution system is visible on the left side of the picture. Each Aero Pot has a nozzle installed on the both ends of a rod with an electric motor in the middle. The e


lectric motor rotates the main rod in 180 degrees, which gives us a full circle if both of the nozzles work properly. If there is a failure on one nozzle, we can perform a workaround procedure and give a motor the instruction to perform full circle rotation. Above the nozzles, plant contain


ers are situated as visualized on the right side of the picture. Aero Pot has this specific circular shape and nozzle installation because of the requirement of misting the roots of plants several times per hour. This design is very energy efficient, cheap, high scalable a


nd easy to control. The future work will be focused on sensor installation and piping of the nutrient distribution system predesign so Aero Pot can work stand-alone or can be connected to other Aero Pots, thus sharing some sensors and actuators which will significantly lower the in


itial costs per one grown plant. 4.1 Experiments and Simulations For the first experimental and simulation work we designed and created software based on genetic algorithm to optimize power consumption of hydroponic or aeroponic system. Using this software user is able to defi


ne various properties and virtually configure hydroponic or aeroponic system. This software allows user to add and remove lights, pumps and define consumption of added devices. In addition user is later asked to specify details about system configuration. Both hydroponic and aeropon


ic systems may be running in different modes. Different plants require different light and water conditions. All these conditions are specified by the user using intuitive graphical user interface. Genetic algorithm designed primarily for power consumption optimization the


n optimizes and create a power schedule for 24 hour window for all the devices in hydroponic or aeroponic system. First experimental work was performed on simple hydroponic system created in our laboratory as Aero Pot creation is in progress. The following picture shows exampl


e of power optimization framework GUI. Fig. 3. Detailed system configuration and growing plant conditions and requirements. Final fitness function of genetic algorithm at this stage of the project consists of 3 elements. These are partial fitness of lights plus partial


fitness of water pumps and penalties defined by user. Both, light and water pumps configuration have to meet the conditions defined by user for every growing plant. Penalties are also defined by user. User can choose to optimize system for plant growth, eliminate peak consumptions or optimi


ze power consumption for specific time of the day. The output of genetic algorithm is the power schedule of lights, light intensities and water pumps cycles during 24 hour time window. Initial experiments showed that plants are doing well while power consumption of the system is drop


ped down. This is achieved with minimum human operator effort. 5 Conclusions This work is aimed to explore some possibilities for improvement of the hydroponic and aeroponic systems by utilization of methods of artificial intelligence. We showed that the demand for clean food becomes in


creasingly alarming as the world population rises. In this paper we provided a brief literature preview, described the basics of aeroponics and hydroponics, their common and distinctive features. In addition some previous work done in this field of research and current trends has been presen


ted. We provided a brief literature preview of intelligent space problem and presented the idea how it can be utilized in greenhouse environments. In the second part of this paper we proposed the control architecture for the aeroponic greenhouse system being developed in our de


partment, which is fully compatible with HAPI. Also the Aero Pot sketches and some of its features were uncovered in this paper. Future work will be focused on finishing the Aero Pot design, construction of an aeroponic greenhouse as proposed in chapter 4 and the commen


cement of work on control algorithms. The first software project being considered at the moment is a power optimization system for both aeroponic and hydroponic configurations. The different devices in a production facility all have timing requirements and energ


y consumption levels, while the cost and availability of power can vary over time. The system will account for alternative energy approaches as well, where factors like weather, wind and sunlight can impact the availability of energy. First experiments showed promising re


sults, but far more experiments need to be done and more and more properties of the hydroponic or aeroponic system need to be taken into account. Acknowledgments Article is the result of the Project implementation: University Science Park TECHNICOM for Innovation Applications S


upported by Knowledge Technology, ITMS: 26220220182, supported by the Research & Development Operational Programme funded by the ERDF. 6 References [1] World Population Prospects: The 2012 Revision, UN Press Release, New York, 13th June 2013. [2] Pittinger D. R.: Introduction to Hor


ticulture, Chapter 2 from California Master Gardener Handbook (Publication 3382), The Regents of the University of California, Division of Agriculture and Natural Retitles, 2002. [3] Doyle O., Aldous D., Barrett-Mold H., Bijzet Z., Darnell R., Martin B., McEvilly G., and Steph


enson R.: 2012 Defining Horticulture, Horticulturist and Horticultural Scientist, Ad Hoc Committee for Global Horticulture Advocacy. Editor: Dr Owen Doyle University College Dublin Ireland. Feb. 2012 [4] NASA Spinoff 2006, Innovative Partnership Program, Publications and Graphics Depart


ment NASA Center for Aerospace Information (CASI), 2006 [5] Thiyagarajan G., Umadevi R., and Ramesh K.: Hydroponics, Science Tech Entrepreneur. January 2007. [6] Sholto D. J.: Advanced guide to hydroponics, No. new edition, Pelham Books, 1985. [7] Nir I.: Growing Plants in Aeropon


ics Growth System, Acta Hort. (ISHS) 126:435448, 1982. [8] Amos J., et. al.: Final Report for the Robotic Construction of a Permanently Manned Mars Base, Mars Investment for a New Generation (M.I.N.G.), NASA 1989. [9] Ritter E., Angulo B., Riga P., Herran C., Relloso J. and San Jose


M.: Comparison of hydroponic and aeroponic cultivation systems for the production of potato minitubers, Potato Research 44 (2001) 127-135, 2001. [10] Morimoto T., Hatou K., and Hashimoto Y.: Intelligent Control for a Plant Production System, Control Eng. Practice, Vol. 4, No. 6, pp. 773-784, 1


996. [11] Lopez-Morales V., Lopez-Ortega O., Ramos-Fernandez J., Munoz L.B.: JAPIEST: An integral intelligent system for the diagnosis and control of tomatoes diseases and pests in hydroponic greenhouses, Expert Systems with Applications 35 (2008) 1506–1512, 2008. [12]


Ferentinos K. P., Albright L. D., Selman B.: Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics, Computers and Electronics in Agriculture 40 (2003) 65-85, 2003. [13] Ferentinos K. P., Albright L. D.: Fault Detection and Diagnosis in


Deep-trough Hydroponics using Intelligent Computational Tools, Biosystems Engineering (2003) 84 (1), 13–30, DOI: 10.1016/S1537-5110(02)00232-5, 2003. [14] Tik L. B., Khuan C. T., Palaniappan S.: Monitoring of an Aeroponic Greenhouse with a Sensor Network, IJCSNS International Journ


al of Computer Science and Network Security, VOL.9 No.3, March 2009. [15] Sahu K., Mazumdar, S. G.: Digitally Greenhouse Monitoring and Controlling of System based on Embedded System, International Journal of Scientific & Engineering Research, Volume 3, Issue 1, January


2012 ISSN 2229-5518. [16] Song Y., Ma J., Zhang X., Feng Y.: Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System, Journal of Networks, Vol 7, No 5 (2012), 838-844, May 2012, doi:10.4304/jnw.7.5.838-844. [17] Ahonen T., V


irrankoski R., Elmusrati M.: Greenhouse Monitoring with Wireless Sensor Network, Mechtronic and Embedded Systems and Applications, 2008. MESA 2008. IEEE/ASME International Conference on , vol., no., pp.403,408, 12-15 Oct. 2008 doi: 10.1109/MESA.2008.4735744 [18] Liu


B., Wang F-Y., Geng J., Yao Q., Gao H., Zhang B.: Intelligent spaces: An overview, Vehicular Electronics and Safety, 2007. ICVES. IEEE International Con-ference on , vol., no., pp.1,6, 13-15 Dec. 2007. [19] Sasaki T., Brscic D., Hashimoto H.: Human-Observation-Based Extractio


n of Path Patterns for Mobile Robot Navigation, Industrial Electronics, IEEE Transactions on , vol.57, no.4, pp.1401,1410, April 2010. [20] Tivatansakul S., Tanupaprungsun S., Areekijseree K., Achalakul T., Hirasawa K., Sawada S., Saitoh A., Ohkura M.: The intelligent space


for the elderly — Implementation of fall detection algorithm, SICE Annual Conference (SICE), 2012 Proceedings of , vol., no., pp.1944,1949, 20-23 Aug. 2012. [21] Qu F., Wang F-Y., Yang L.: Intelligent transportation spaces: vehicles, traffic, communications, and beyond,"Communicati


ons Magazine, IEEE , vol.48, no.11, pp.136,142, November 2010. [22] Lu F., Tian G., Zhou F., Xue Y., Song B.: Building an Intelligent Home Space for Service Robot Based on Multi-Pattern Information Model and Wireless Sensor Networks, Intelligent Control and Automation, 3, 90


-97, 2012. [23] Lu S., Qi W.: Navigation and positioning research of service robot based on intelligent space, Automation and Logistics, 2009. ICAL '09, vol., no., pp.2015, 2017, 5-7 Aug. 2009 View publication stats


Received:	16	September	2020  |  Revised:	6	February	2021  |  Accepted:	16	February	2021 DOI: 10.1002/pld3.312 O R I G I N A L R E S E A R C H Aeroponic systems: A unique tool for estimating plant water relations and NO3 uptake in response to salinity stress Endale Geta Tafesse1  | Moses Kw

Aeroponic systems: A unique tool for estimating plant waterrelations and NO3 uptake in response to salinity stress


ame Aidoo1,2  | Naftali Lazarovitch1  | Shimon Rachmilevitch1 1French	Associates	Institute	for	Agriculture	 and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, BenGurion University of the Negev, Midreshet BenGurion, Israel 2Department of Agro Enterprise Dev


elopment, Ho Technical University, Ho, Ghana Correspondence Shimon	Rachmilevitch,	French	Associates	 Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, BenGurion University of the Negev, Sede Boqer Campus, Midreshet BenGurion, Israel. E


mail: rshimon@bgu.ac.il Funding information The authors thank Gabi Rubanenko for his generous donation towards the establishment of the system. This research was partially supported by the Israeli Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the p


rogram The Root of the Matter: The root zone knowledge center for leveraging modern agriculture. Abstract The study of transpiration, water, and nutrient uptake during abiotic stress in the root zone is hindered because of the hidden nature of the root zone. In this study, a modified aerop


onic system was used to evaluate whole plant transpiration, nitrate and water uptake in the growth and development of tomato plants in response to salinity. Tomato seedlings were exposed to three levels of salinity (1.5, 4.5, and 9 dSm−1) and three levels of nitrate (1, 4, and


8 mM NO3) in a separate experiments conducted concurrently. Whole plant transpiration, water and nitrate uptake were estimated. Our study revealed that ~30 to 35 days after treatment (DAT), water uptake rate per plant increased from a common initial rate of about 0.05 to 1.1, 0.6


, and 0.4 kg/day at 1.5, 4.5, and 9 dSm−1 respectively. The NO3 uptake rates in tomatoes grown in 1 and 4 mM NO3 were 5.5 and 22% respectively, of the uptake of tomatoes grown in 8 mM NO3. The estimation of nitrate uptake and lower sensitivity to salinity stress in the aeroponic showed the effec


tiveness and cost efficiency of the system in the cultivation of vegetables during abiotic stresses. The novelty of the system described is the continuous estimation of root and nutrient uptake by the whole plant at any given time. K E Y W O R D S abiotic stress, aeroponic, nutrient uptake,


tomato, water relations 1 |  I NTRO D U C TI O N research tool in root physiology (Barak et al., 1996) and is recommended as a technique for steadystate control of nutrients, gas exAeroponics is a plant culture technique employed for the growth of changes, root temperature, an


d moisture (Aidoo et al., 2017; Zobel plant where the roots are either continuously or periodically misted et al., 1976). with a nutrient solution; it may be regarded as a variant of hydroponThe use of aeroponic systems as a research tool has demonics where plant roots are const


antly cultivated in a nutrient solution strated the absence or insignificance of an additional stress asso(Rubanenko & Hilitsky, 2011). Aeroponics has long been used as a ciated with stress of interest, for example hypoxia, under/over Abbreviations: DAT, days afte


r treatment; EC, electrical conductivity. Endale Geta Tafesse and Moses Kwame Aidoo contributed equally to the study. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original


work is properly cited. © 2021 The Authors. Plant Direct published by American Society of Plant Biologists and the Society for Experimental Biology and John Wiley & Sons Ltd. Plant Direct. 2021;5:e00312.	 https://doi.org/10.1002/pld3.312 wileyonlinelibrary.com/journal/pld3 	 | 	1 of 8 		 2 of


8  |     irrigation, and deposition of salt in the root zone which might be the estimation of the transpiration rate which allows water and nuassociated with application of salinity in the soil medium. The metrient uptake of the plant in response to salinity stress at


the root chanical force that impaired the development and growth of roots zone. These parameters are difficult to estimate under other growth is also minimized (Peterson & Krueger, 1988). Another advantage of conditions such as hydroponics and soil. Using tomato plants, we aerop


onic systems is their easytouse method of monitoring plant demonstrated that aeroponic systems can be utilized as a tool for nutrition, pH, and electrical conductivity (EC; Tafesse, 2014). This continuous measurement of plant water uptake during salinity and type of sys


tem also supports continuous measuring of plant shoots different levels of plant nutrition in the root zone. Most importantly, and roots in both nondestructive and destructive methods for anawe also showed estimation of total plant transpiration during the lytical analysis in the


laboratory (Aidoo et al., 2017). Aeroponic and growth period. hydroponic systems are both soilless plant cultures. In comparing soil and soilless systems, aeroponic systems allow the direct observation of plants without disturbances, so that necessary actions can 2 |  M ATE R I A


L S A N D M E TH O DS be taken before any problem becomes irreversible. However, aeroponics has not been more extensively used be2.1 | Description of aeroponic system cause of incomplete knowledge about the operational parameters, and the difficulty in maintaining the operating syste


m (Weathers & Aeroponic systems are mostly, set up to control the injection of Zobel, 1992). The absence of substrate buffering the root zone in nutrient solution to the root zone for the growth of plants. These the system makes plant vulnerable to total collapse


within a relasystems are mainly fixed with limited accessories for the delivtively short period under electrical power outages and/or technical ering of nutrient solutions. In this study an aeroponic apparatus failure. In addition, the system requires constant se


rvicing opera(Figure	1)	consisting	of	circular	aqua	pots	(Agro-	innovations	LLC)	 tions,	which	may	be	costly.	For	instance,	the	pumps	and	misters	remade from polyvinylchloride (PVC) with a diameter of 50 cm and a quire maintenance and may be prone to potential component failure depth of


14 cm was used. Within each aeroponic aqua pot, misters (Aidoo et al., 2017). The failure or clogging of misters may restrict (Coolnet, Netafim) were installed to spray the desired fine mist dithe plant's access to water, causing it to lose turgidity and wilt, which rectly	onto	the	roots	of


the	plants	in	the	root	zone.	Four	aqua	pots	 may be irreversible. were each fixed in the four large slots located on the top of the ion Transpiration rate is affected by the water vapor concentration tanks (chambers) built with ion sheets on both sides insulated with in the surrounding air


and the leaf temperature, as well as the boundStyrofoam to prevent influence from environmental factors durary layer and stomatal resistances (Kubota, 2016). Plants transpire ing treatments. Builtin air conditioners, heaters, thermocouples most of the water they abso


rb from the soil in exchange for CO2 they obtain from the atmosphere, affecting the process of photosynthe(Type E; not applicable to this study) and reservoir tanks to hold nutrient solution were installed in the chambers. The covers of sis. Understanding transpiration when plants a


re exposed to salinity the aqua pots were enforced with a thick black Styrofoam layer to and inadequate plant nutrition will contribute to breeding of highly create darker environmental conditions for the roots and to preefficient plants in response to stress. vent algal growth and develop


ment inside the aqua pots. Nutrient It was, therefore, hypothesized that a wellstructured aeroreservoirs from which the nutrient solution can be directly drawn ponic system design will enhance our understanding and facilitate to the plant roots were placed on a digi


tal scale interfaced to a F I G U R E 1  A sketch showing the aeroponic system, its accessories and various parts setup in a greenhouse located at BenGurion University, Sede Boqer Campus, Israel TAFESSE ET Al.F I G U R E 2  Experimental design setup in the greenhouse used for salini


ty (a; 1; 2; 3) and nitrate (b; 4; 5; 6) treatments. The design indicates the six aeroponic systems with four aqua pots each of the system and the number of plants per aqua pot (4) per aeroponic system (16)     |  3 of 8 computer via data loggers (CR1000, Campbell Scientific), and one addit


ional reservoir, also located on a digital scale, served as the main title of the nutrient solution to the reservoirs located in the Three reservoirs (for the three salinity levels) with a capacity of 0.2 m3 and two additional reservoirs with a capacity of 1 m3 were used in this study. Th


e three reservoirs with a capacity of 0.2 m3, aeroponic	 chambers.	 A	 booster	 pump	 (Flojet)	 were	 used	 to	 draw	 from which nutrient solution was directly drawn to the plants were the nutrient solution from the reservoir tank into the respective placed on digital scal


es interfaced to a computer via data loggers aeroponic aqua pots. The height difference between the aqua pot (CR1000, Campbell scientific) so that continuous weight changes and the reservoir were used to return the solution to its respective reservoir after the roots were spra


yed with the nutrient mist. In addition, EC meters (ES2, Decagon devices) were inserted into can be recorded and from which transpiration rate can be calculated. In the first experiment two big reservoirs with a capacity of 1 m3 were filled with the nutrient solution of 1.5 and 9


dSm−1 (A1.5 and each of the solution reservoirs connected to data loggers monitorA9, respectively) from which the three reservoirs were filled on a ing the EC levels of the irrigation water. Builtin notification alerts daily basis. The reservoir used for A1.5 and A9 we


re directly conwere activated in case there are mechanical faults in the operanected to their respective bigger reservoirs so that nutrient solution tions	of	the	system	(Figure	1). would draw into them based on a predetermined level of nutrient 2.2 | Plant materials, salinit


y, nitrate treatments, and experimental design Two different experiments concurrently conducted in a six improved aeroponic	systems	three	for	each	experiment	(Figure	1)	setup	in	the	 (weight of the tanks) whereas the reservoir for A4.5 (solution with EC 4.5 dSm−1) was filled by mixing solution


s from the two big tanks by weight ratio. A4.5 and A9 desired salinity levels were obtained by applying NaCl in a halfstrength Hoagland nutrient solution (Hoagland & Arnon, 1950), which already had an EC of 1.5 dS m−1 hence A1.5 treatment.	For	A9,	36	mM	of	NaCl	was	applied	in	the	half-	str


ength	 Hoagland nutrient solution, resulting in an EC of 9 dSm−1, whereas greenhouse at BenGurion University of the Negev, Sede Boqer A4.5 was formulated by mixing the two solutions (A9 and A1.5) at Campus. A total of three different aeroponic systems were used for three salinity


levels with electrical conductivity (EC) values of 1.5, 4.5, and 9 dS m−1 (thereafter, A1.5, A4.5, and A9) and a total of additional three levels of NO3 (1, 4, and 8 mM) in a separate experiments. Each treatment was assigned to a separate aeroponic system with four aquap


ots fixed on top of it. An aquapot takes 4 individual plants which sum up to 16 plants per treatment per aeroponic system. Six out of these 16 individual plants were measured as replicates in this study	(Figure	2). Seedlings of Solanum lycopersicum L. var. Mose were obtained from


Zeraim Gedera Syngenta, Israel. After 30 days (end of July) of nursing the tomato seeds in a peat soil medium until they reached four fully produced leaves stage, the roots of the seedlings were gently washed and transferred to the aeroponic aqua pots, and after two weeks of acc


limatization to the system, the plants were subjected to treatments. mass ratio of 1:1.88 of A9 and A1.5, respectively, which resulted in an EC of 4.5 dSm−1. The NaCl was added proportionally to have an equivalent effect on the EC of the nutrient solution so that the competition bet


ween Na and K was minimized. The second experiment involved in the evaluation of nitrate levels were also conducted in the three separated aeroponic systems (as explained above) with three levels of NO3 (1, 4, and 8 mM) added to the halfstrength Hoagland nutrient solu


tion tested on tomatoes grown under salinity with EC of about 4.5 dSm−1 grown in an aeroponic system. Each level of NO3 was replicated six times as explained above during salinity experiment. In the preparation of NO3 stock solution, 202 g of KNO3 salt were mixed in a liter of wate


r resulting 2 M KNO3 and 2 M Ca(NO3)2.4H2O were also prepared by mixing 472 g of Ca(NO3)2.4H2O salt in a liter of water. In the application of the treatments, 1 mM NO3 was made by applying 0.25 ml, each from stock solutions of KNO3 and Ca(NO3)2.4H2O, per liter of water applied to the plants. 4


mM NO3 was made by applying 1.0 ml, TAFESSE ET Al.4 of 8  |     each from stock solutions of KNO3 and Ca(NO3)2.4H2O, per liter of water applied to the plants. mM NO3 was made by applying 2.0 ml, each from stock solutions of KNO3 and Ca(NO3)2.4H2O, per liter of water also applied to the plan


ts The pH of the nutrient solutions was maintained between 6 were filled with fresh nutrient solution so that the amount of nitrate lost from the tanks due to plant uptake could be calculated. 2.5 | Plant morphological characteristics and 6.5. In addition, EC meter sensors wer


e inserted into each of Growth parameters such as shoot fresh and dry weight, root fresh the solution reservoirs and connected to data loggers so that the and dry weight including yield and fruit dry weight were measured EC level of the water can be continuously monitored. The


computer at the end of the experiment thus after 90 days. Both below and control spraying irrigate the plants for 1 min after every 6 min interabove ground fresh weights were immediately determined whereas val depending on the growth stage (size) of the plants and the amrespe


ctive dry biomasses were determined after ovendrying at bient temperature. The ambient temperature recorded throughout 65°C for 72 hr. the period of the experiment was 27 and 16°C (day and night temperatures respectively). The aeroponic chambers were subjected to the temperature o


f day light and other environmental factors in the 2.6 | Data analysis greenhouse. Analysis of variance (ANOVA) and posthoc Tukey's HSD test were performed using statistica (version 11 Stat soft Inc.) to evaluate dif2.3 | Estimation of transpiration and water uptake ferences among the tr


eatments for the measured plant parameters. The independent variables used as categorical predictors are salinity Three reservoirs each with a capacity of 200 L, from which nutrient levels within each growth systems and the dependent variables were solutions were directly irrigated to the pl


ants, were placed on digithe measured plant parameters. tal balances interfaced to a computer via data loggers. Continuous mass change were recorded from which the transpiration rate was calculated based on the mass change of the nutrient solution on the 3 |  R E S U LT S A N D D I S CU S


S I O N scale due to plant uptake in relation to change in time. This method is technically similar to weighing lysimeters, but as there was no drainThis paper described the effectiveness of using an aeroponic sysage in the closed aeroponic system and evaporation from the system


tems regarding the measuring of whole plant transpiration rate, plant was negligible, the mass decrease of the reservoirs was thus mainly water, and nutrient uptake in response to salinity and different levels due to plant water uptake. The mass of the tanks was recorded every of nitrates. T


he provision of more accessories to enhance aeroponic 15 min. 2.4 | Estimation of nitrate uptake system operations in this study, confirmed its effectiveness for experimental	purposes	(Figure	1).	The	use	of	aeroponic	systems	offers	 access to whole plant roots, preventing the loss of a signi


ficant portion of roots during washing from the soil. This system could also improve the roots taxa identification allowing speciesspecific quesThe plant nitrate uptake was estimated in the aeroponic system tions to be posed (Rewald et al., 2012). based on nutrient c


oncentration depletion or uptake determined by To understand the rate of water loss, we measured the hourly calculating the differences between change in the volume of the nutranspiration rate of tomatoes, which varied throughout the day. trient on time points 1 and 2 mult


iplied by the nitrate concentration These variations increased during the morning until the rate peaked in the irrigated water solution on time point 1 and 2, respectively around 12:00– 15:00 hr of the day during the first two months (Barak et al., 1996; Cabrera et al.,


1995). (August	 and	 September)	 of	 the	 experiment	 (Figure	 3a–	d).	 In	 the	 Nutrient uptake rate = (V1 × C1) − (V2 × C2) last month of the experiment, the hourly transpiration peak was recorded	around	9:00–	12:00	hr	of	the	days	in	October	(Figure	3e–	i).	 This might have been due to the


end of summer pushing the transpiwhere V1 and V2 are the volumes of the nutrient solution at time points ration demand to reach its peak earlier in the day as the days short1 and 2, and C1 and C2 are the nutrient concentrations (mmol/L) at time ened. The maximum hourly water uptake rates p


er plant observed point 1 and 2. This gave an idea of the amount of nitrate taken up by during 28– 72 DAT during the afternoon were about 0.16, 0.08, and the plant at any given time point. The nitrate concentration was then 0.03	kg	for	A1.5,	A4.5,	and	A9,	respectively	(Figure	3d–	h).	However


,	 examined by ultraviolet absorption using a spectrophotometer (Epoch, at the beginning (1– 27 DAT) and the last (73– 81) hourly water upBioTek) at 220 nm to obtain the nitrate reading and at 275 to correct take	rates	per	plant	recorded	low	values	(Figure	3a–	c,i).	Such	a	large	 the interf


erence due to dissolved organic matter based on the method variation in the transpiration flux in a given day and during the entire suggested by Greenberg et al., (1992). The nitrate concentration meaplant growth period indicates the dynamic nature of the water upsurement was performed eac


h day before and after the nutrient tanks take process and size of the plants, which is influenced by short and TAFESSE ET Al.    |  5 of 8 F I G U R E 3  Diurnal pattern of water uptake rates in tomatoes under the three salinity levels (A1.5, A4.5, and A9) in the aeroponic system at differ


ent stages of plant growth based on days after treatment. The values mentioned at each point are the average of nine measurements from consecutive days taken at similar times with six replicates longterm light and temperature and long term plant growth during the experimental period. The quick


response of plants to such environmental changes have been highlighted by Munns (2002). In the average daily water uptake rate, 50 and 75% reductions were recorded in A4.5 and A9, respectively, compared to the control (A1.5) during peak transpiration periods (25– 65 DAT). However


, during the first ~15 DAT, no differences were observed between the three salinity levels. In the first ~30– 35 DAT, the water uptake rate per plant increased from a common initial rate of about 0.05– 1.1, 0.6,	 and	 0.4	 kg/day	 in	 A1.5,	 A4.5,	 and	 A9,	 respectively	 (Figure	 4)


.	 Such an increase in the plant water uptake rate over time was mainly due to the active plant growth in terms of number and size of leaves, total leaf area, plant height, and total biomass accumulation as reported in Bhantana and Lazarovitch (2010). The cumulative water uptak


e rates in each of the salinity level treatments were obtained by summing the respective daily uptakes (Figure	5).	Similar	to	the	hourly	and	daily	rates,	there	was	no	significant difference between the salinity levels during the first 18 DAT, but at the end of the growth perio


d, the cumulative transpiration F I G U R E 4  Water uptake rates per plant per day of tomatoes grown in an aeroponic system under the three salinity levels (A1.5, A4.5, and A9) over the entire growth period. Each point is the sum of 96 measurements recorded per day (recorded every 15 min)


with six replicates. Variations in water uptake rate were first seen about 12 days after treatment (DAT) rate of A1.5 was found to be more than two and fivefold higher significant differences in the slope between the treatments. There than A4.5 and A9, respectively. Genera


lly, at 25 DAT, the increase was a significant reduction in the hourly, daily and cumulative tranin the cumulative transpiration rate was approximately linear with spiration rates due to salinity, and this finding is in agreement with a TAFESSE ET Al.6 of 8  |     F I G U R E


5  Cumulative water uptake rate per tomato plant grown in an aeroponic system under saline water (A1.5, A4.5, and A9) during the entire growth period. Values are averages of six replicates F I G U R E 7  NO 3 uptake of tomatoes grown under the three NO3 concentration (1 mM NO3, 4 mM NO3 an


d 8 mM NO3). The measurement was carried out for approximately 30 days from 20– 55 DAT. Values are averages of six replicates F I G U R E 6  Shoot	dry	weight	(SDW),	fruit	dry	weight	(FDW)	 and root dry weight (RDW), of tomatoes grown in aeroponic under three levels of salinity. Each value is a


n average of six measurements ± SE (six replication). Columns marked with similar letters compare across the treatments are not significantly different. (Post hoc Tukey test p < .05) F I G U R E 8  Relationship between NO 3 uptake and water uptake of tomatoes grown under different NO3 concent


ration levels (1 mM NO3, 4 mM NO3, and 8 mM NO3) in an aeroponic system under saline conditions. Values are averages of six replicates number of previous studies conducted on tomatoes and other crops’ Tafesse (2014). This shows that perhaps plants grown in the soil is responses to salinity


(Maggio et al., 2007; Munns, 2002; Romerosensitive to salinity stress than aeroponics given the high deposiAranda et al., 2001). Salinity mostly causes osmotic imbalances and tion of Na in the root zone of the soil. This may be due to gradual reduced water uptake and t


ranspiration, as well as reduced yields build up and accumulation of salts in the root zone regardless of the (BenGal et al., 2008). Increase influx and shoot accumulation of soleaching fractions in the soil. The reduced magnitude of biomass dium has been linked to its


toxicity and this has been reported to in the aeroponic system might have been due to enhanced uptake reduced biomass and yield significantly (Kronzucker et al., 2013). and utilization of water and nutrients even at a moderately higher In measuring the plant biomass, significant


differences level of salinity stress. The reduction of dry matter may be due to (p < .001) were observed among the three levels of salinity stress. A1.5 treated plants had a higher total biomass than A4.5 and A9 several adverse effects of salt stress such as specific ion toxicities


, nutrient deficiencies, retardation of water uptake and nutritional (Figure	 6).	 The	 reduction	 of	 total	 dry	 biomass	 was	 due	 to	 the	 increase of salinity treatments from EC 1.5 to 4.5 dSm−1. However, imbalances in plants which affect enzymatic and physiological fun


ctions reducing growth, dry matter accumulation and yield of crops the biomass reduction (36%) in aeroponics was better compared to (Grattan & Grieve, 1999; Koyro, 2006; Mittler, 2006; Munns, 2002; 55% reduction in the soil at similar salinity treatments reported by Sagi et al., 1998). T


AFESSE ET Al.F I G U R E 9  Relationship between NO 3 uptake and water uptake of tomatoes as a function of concentration. The values presented here are the overall averages of NO3 and water uptake rates per plant per day during the entire growth period. Values are averages of six replicates


|  7 of 8 The NO3 uptake in the tomatoes treated with different NO3 concentrations showed significant differences between the treatments. The NO3 uptake rates of tomatoes grown in 1 and 4 mM NO3 concentrations were only 5.5 and 22% of the uptake of tomatoes gro


wn in 8 mM NO3	(Figure	7).	An	increase	in	the	NO3 concentration in the nutrient solution resulted in greater NO3	uptake	(Flowers	&	 Yeo, 1986), and this agrees with the finding that an increased N level in the solution significantly increases the N uptake in tomato plants’ shoot NO3 concentrati


ons (Abdelgadir et al., 2005). The factor that limits a low uptake rate is the amount of nutrients available at the root surface, as reported by (Ingestad & Agren, 1988), in that the relative nutrient uptake rate is proportional to the relative plant growth rate. The nitrogen uptake rate is


positively correlated with the plant water uptake rate, which is again affected by the plant growth rate (Figure	 8).	 The	 regression	 line	 slopes	 for	 the	 three	 NO3 levels decreased with concentration. This indicates that the plant nitrate uptake rate is dependent on the nitrate con


centration, and that a high concentration	leads	to	a	higher	rate	of	nitrate	uptake	(Figure	9).	In	 the study conducted by Abdelgadir et al., (2005), a close relationship was observed between cumulative transpiration and NO3 concentration in tomatoes. Below and above dry biomass were m


easured as shoot and root F I G U R E 1 0  Biomass of tomatoes grown at the three levels of NO3 concentration expressed as shoot dry weight (SDW) and root dry weight (RDW). Columns marked with similar letters within each biomass parameter are not significantly different (Post hoc Tukey test


p < .05). Error bars are standard errors (n = 6) dry weight, and fruit yield. Different levels of NO3 concentration 4 |  CO N C LU S I O N S significantly (p < .05) affected plant biomass, except root dry weight (Figure	10).	The	higher	the	NO3	concentration,	the	higher	the	bioAeropo


nic systems can be utilized for the fast, accurate and continumass obtained. The reduction of dry matter is as a result of reduced ous measurement of plant responses to various environmental stresses NO3 concentration from 8 to 4 mM (20%) and from 8 to 1 mM (64%). However, the difference


between tomatoes supplied with 4 and 8 mM NO3	was	not	significant	(Figure	10).	Several	studies	showed	 progressive increase of dry matter accumulation with increase of especially in the root zone. Our findings have proven that in aeroponic systems, the root zone can be totally con


trolled during the experimental period. Irrigation can be adjusted at any time according to the demands of the plants, the treatments and the objectives of the study. Water and NO3 levels (Abdelgadir et al., 2005). However, further increase of nutrient uptake by plants can be contin


uously and accurately measured NO3 beyond the critical level, results in reduction in growth and at any stage of the plant growth and the experiment. Tomatoes grown biomass accumulation (Gastal & Lemaire, 2002) as well as shoot to in the aeroponic system were less sensitive to sali


nity. In aeroponics, root ratios (Bennett et al., 1989). The reason for the nonsignificant nitrate supply to the plants can be minimized without significant reducdifference in total biomass between tomatoes supplied with 4 and tion of the plant performances due to high nitrate uptake. The


most 8 mM NO3 is that the plants might have reached the NO3 critical content for maximum growth rate. interesting aspect of our findings is the continuous estimation of exact amount of water transpired by plant at any given time. TAFESSE ET Al.8 of 8  |     AC K N OW L E D G M


E N T S The authors thank Yuval Shani for his skillful assistance with the operation and maintenance of the system, Liron Summerfield for all her great help. C O N FL I C T O F I N T E R E S T The authors declare that they have no competing interests. AU T H O R S ’ C O N T R I B


U T I O N S EGT performed the experiment, analyzed data, and helped in writing the manuscript. MKA organized the data and helped in summarizing and writing the manuscript. NL designed the experiment. NL and SR supervised the discussion of the results. NL and SR were in charge of the researc


h project. All authors read and approved the manuscript. O R C I D Endale Geta Tafesse https://orcid.org/0000-0001-6967-0756 Moses Kwame Aidoo https://orcid.org/0000-0003-0183-4771 Naftali Lazarovitch https://orcid.org/0000-0002-3630-5696 Shimon Rachmilevitch https://orcid.org/0


000-0003-3600-5949 R E F E R E N C E S Abdelgadir,	E.	M.,	Oka,	M.,	&	Fujiyama,	H.	(2005).	Characteristics	of	nitrate uptake by plants under salinity. Journal Plant Nutrition, 28, 33– 46. https://doi.org/10.1081/PLN20004 2156 Aidoo,	M.	K.,	Sherman,	T.,	Lazarovitch,	N.,	Fait,	A.,	&	Rachmilevi


tch,	S.	 (2017). A bell pepper cultivar tolerant to chilling enhanced nitrogen allocation and stress related metabolite accumulation in the roots in response to low root zone temperature. Physiologia Plantarum, 161, 196– 210. https://doi.org/10.1111/ppl.12584 Barak, P., Smith, J. D., Kruger, A.


R., & Pete, L. A. (1996). Measurement of shortterm nutrient uptake rates in cranberry by aeroponics. Plant, Cell and Environment, 19, 237– 242. https://doi.org/10.1111/ j.13653040.1996.tb002 46.x BenGal, A., Ityel, E., Dudley, L., Cohen, S., Yermiyahu, U., Presnov,


E., Zigmond, L., & Shani, U. (2008). Effect of irrigation water salinity on transpiration and on leaching requirements: A case study for bell peppers. Agricultural Water Management, 95, 587– 597. https://doi. org/10.1016/j.agwat.2007.12.008 Bennett, J. M., Mutti, L. S. M., Rao, P.


S. C., & Jones, J. W. (1989). Interactive effects of nitrogen and water stresses on biomass accumulation, nitrogen uptake, and seed yield of maize. Field Crops Residue, 19, 297– 311. https://doi.org/10.1016/03784290(89)90100 7 Bhantana, P., & Lazarovitch, N. (2010). Evapotranspiration, cro


p coefficient and growth of two young pomegranate (Punica granatum L.) varieties under salt stress. Agricultural Water Management, 97, 715– 722. https://doi.org/10.1016/j.agwat.2009.12.016 Cabrera, R. I., Evans, R. Y., & Paul, J. L. (1995). Cyclic nitrogen uptake by greenhouse


roses. Scientia Horticulturae, 63, 57– 66. https://doi. org/10.1016/03044238(95)00789 V Flowers,	T.	J.,	&	Yeo,	A.	R.	(1986).	Ion	relations	of	plants	under	drought	 and salinity. Australian Journal of Plant Physiology, 13, 75– 91. Gastal,	F.,	&	Lemaire,	G.	(2002).	N	uptake	and	distributio


n	in	crops:	An	agronomical and eco physiological perspective. Journal of Experimental Botany, 53, 789– 799. https://doi.org/10.1093/jexbo t/53.370.789 Grattan, S. R., & Grieve, C. M. (1999). Salinitymineral nutrient relations in horticultural crops. Scientia Horticultutre, 8, 127– 157. Green


berg, A. E., Clesceri, L. S., & Eaton, A. D. (1992). Standard methods for the examination of water and wastewater. DC American Public Health Association. Hoagland, D. R., & Arnon, D. I. (1950). The waterculture method for growing plants without soil. In California agric


ultural experimental station circular (Vol. 347). University of California. Ingestad, T., & Agren, G. (1988). Nutrient uptake and allocation at steadystate nutrition. Physiology Plantarum, 72, 450– 459. https://doi. org/10.1111/j.13993054.1988.tb091 50.x Koyro, H. W. (2006). Effect of


salinity on growth, photosynthesis, water relations and solute composition of the potential cash crop halophyte Plantago coronopus (L.). Environmental Experimental Botany, 56, 136– 146. https://doi.org/10.1016/j.envex pbot.2005.02.001 Kronzucker, H. J., Coskun, D., Schulze, L. M., Wong,


J. R., & Britto, D. T. (2013). Sodium as nutrient and toxicant. Plant and Soil, 369, 1– 23. https://doi.org/10.1007/s1110 401318012 Kubota, C. (2016). Plant Factory: An indoor vertical farming system for efficient quality food production (T. Kozai, G. Niu, & M. Takagaki, Eds.). Els


evier Inc, ISBN: 9780128017753, pp. 151– 164. Maggio, A., Raimondi, G., Martino, A., & Pascale, S. D. (2007). Salt stress response in tomato hybrid beyond the salinity tolerance threshold. Environmental Experimental Botany, 59, 276– 282. Mittler, R. (2006). Abiotic stress, the field envi


ronment and stress combination. Trends in Plant Science, 11, 11– 19. https://doi.org/10.1016/j. tplan ts.2005.11.002 Munns, R. (2002). Comparative physiology of salt and water stress. Plant Cell and Environment, 25, 239– 250. https://doi. org/10.1046/j.00168025.2001.00808.x Pe


terson, L. A., & Krueger, A. R. (1988). An intermittent aeroponics system. Crop Science, 28, 712– 713. https://doi.org/10.2135/crops ci1988.00111 83X00 28000 40033x Rewald, B., Meinen, C., Trockenbrodt, M., Ephrath, J. E., & Rachmilevitch, S. (2012). Root taxa identification in pl


ant mixtures – Current techniques and future challenges. Plant and Soil, 359, 165– 182. RomeroAranda, R., Soria, T., & Cuartero, J. (2001). Tomato plantwater uptake and plantwater relationships under saline growth conditions. Plant Science, 60, 265– 272. Rubanenko, G., & Hilitsky, V


. (2011). Soilfree cultivation system. Agroinnovations LLC, USA Patent 2011, page 1. Sagi, M., Dovrat, A., Kipnis, T., & Lips, H. (1998). Nitrate reductase, phosphoenolpyruvate carboxylase, and glutamine synthetase in annual ryegrass as affected by salinity and ni


trogen. Journal Plant Nutrition, 21, 707– 723. Tafesse, E. G. (2014). Aeroponics and abiotic stress relationships on tomato. MSc. Thesis, BenGurion University of the Negev. Weathers, P. J., & Zobel, R. W. (1992). Aeroponics for the culture of organisms, tissues and cells.


Biotechnology Advances, 10, 93– 115. Zobel, R. W., Del Tredici, P., & Torrey, J. G. (1976). Methods for growing plants aeroponically. Plant Physiology, 57, 344– 346. S U P P O R T I N G I N FO R M AT I O N Additional Supporting Information may be found online in the Supporting In


formation section. How to cite this article: Tafesse EG, Aidoo MK, Lazarovitch N, Rachmilevitch S. Aeroponic systems: A unique tool for estimating plant water relations and NO3 uptake in response to salinity stress. Plant Direct. 2021;5:e00312. https://doi. org/10.1002/pld3.312 TAFESSE ET Al.