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Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System

Vasileios Thomopoulos
Dionysios Bitas
Kyriakos-Nikos Papastavros
Dimitris Tsipianitis
2 and
Angeliki Kavga
Department of Computer Engineering & Informatics, University of Patras, 26504 Patras, Greece
Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece
Department of Agricultural Science, University of Patras, 26504 Patras, Greece
Author to whom correspondence should be addressed.
Agronomy 2021, 11(2), 405;
Submission received: 7 January 2021 / Revised: 15 February 2021 / Accepted: 17 February 2021 / Published: 23 February 2021


The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect problems promptly. In recent years, there has been an interest in the development of autonomous agbots equipped with agricultural sensors. Several ground-based AGV (automated guided vehicles) and UAV (unmanned aerial vehicles) systems have been developed for use in open-air plots. A key feature of all these innovative systems is spectroscopy, the development of which has been assisted by the surveillance capabilities and speed of modern-day UAVs (drones). However, there is a lag in the use of spectroscopy inside greenhouses since UAVs do not move freely indoors. In this paper, we propose as a solution a three-device (3DS) system.

1. Introduction

Today, precision agriculture [1] has taken off with the development of autonomous UAVs (Unmanned Aerial Vehicles) also known as drones that help farmers map large areas in a fast and economical way [2,3]. The UAVs provide farmers with data sets, from RGB, spectral, thermal and LiDAR sensors that they carry. Additionally, the data captured by UAVs need to be complemented by other variables such as local climate and soil conditions. A variety of fixed ground stations are cropping up in fields and greenhouses all around the world, collecting soil data and the local climate information. All this information is then fed back to an AI engine to produce an actionable prescription map of fertilizer, irrigation or crop protection needs.
However, a sensor placed at a fixed point in a field or a greenhouse does not necessarily record the critical conditions of temporal and spatial variability necessary to make informed decisions. Furthermore, greenhouse installations lack the benefits of stress maps and prescription maps that can be produced through UAV scouting, as is the case with open fields.
From the thorough focused research, we have conducted and the review of the literature about agriculture cablebots, we may safely presume that agriculture cablebots and agbots is a new scientific area that is now beginning to develop. Accordingly, the existing bibliography is very limited.

2. Related Work

Cablebots to date are mostly experimental devices. The project aim is to introduce precision farming and smart irrigation technologies in a more efficient way, with our integrated Three-Device System (hereinafter referred to as 3DS). The development of this system aims to increase the quantity and quality of agricultural produce, reduce the carbon footprint of the growing process and provide producers with a broad set of usable data, giving them access to the complete picture of their crop at all times.
The proposed 3DS early warning and smart irrigation [4,5] solution for farmers will comprise of a set of technologies that could be applied either as standalone autonomous solutions or as an integrated system of all the above technologies, complete with a man-machine software interface for overall control and final decision making.
The most important microclimate factors for the quality and productivity of plant growth are temperature, humidity, solar radiation and photo-synthetically active radiation (PAR) [6]. Continuous monitoring of these environmental variables provides valuable information to the grower to better understand, how each factor affects growth and how to maximize crop productiveness [7].

3. System Description

The proposed solution is a three-device (3DS) system consisting of:
A solar-powered cablebot equipped with a spectroscopic camera and an array of other sensors (Figure 1) that will collect data much like a drone in an open-air crop [8,9]. The device is hung from an overhead cable, hence the name cablebot or it can be attached to a slider overhanging from the greenhouse roof.
A flow control valve through which the system will autonomously control the greenhouse irrigation network.
A four-legged ground robot (agbot), a ground-based sensor carrier that can roam the greenhouse and collect data (Figure 2).
Also, we will develop a user interface app code-named 3DS App. This web-based application will act as a portal from which growers will receive processed data from their smart agriculture system on their computer, tablet or mobile device. The robotic system is installed and operates in a pilot production greenhouse at the University of Patras.
The idea is to develop a cost-effective system of autonomous moving platforms equipped with a necessary array of sensors for outdoor and indoor agricultural use. These moving platforms, be it aerial cable bots or ground AGVs, will capture data much like a drone would do inside greenhouses and provide microclimate data and soil sensitive information. With greenhouses and small to medium-sized plots in mind, cable bots present an ideal cost/benefit ratio compared to UAVs. To complement the aerial device, a four-legged agricultural bot (agbot) is also under development that will collect measurements and perform tasks that need to be conducted from the ground, such as soil measurements, weeding, etc. We opted to use a quadruped agbot instead of a wheeled robot, because of the greater better degrees of freedom of movement, the avoidance of obstacles from the skeletal elements of the greenhouse, the arched and not smooth ground irrigation system components, and finally high planting density (Table 1).
This project aims to introduce IoT technologies into greenhouse cultivations [10] turning them in effect into smart installations. It also challenges the authority of drones in precision agriculture scouting, since our devices are also suitable for open-air use, albeit geared towards micro-farm use.
Also, our ground roaming agbot is a four-legged dog resembling AGV that can easily overcome obstacles and perform a variety of tasks on uneven ground, as opposed to existing wheeled examples that usually need clear pathways to perform their tasks. It is about time precision agriculture entered greenhouses and became affordable to small plot owners.
Development of a simple solar-powered cablebot (cablebot: a robot that runs suspended from a cable) (Figure 3). A device that will run overhead across greenhouses and small plots, returning data to the plot owner and controlling an electric valve as part of a smart irrigation/fertilization installation. This device will be called Device Aeir (D1). Additionally, a proprietary electric (solenoid) valve will also be designed, with the name Device Flow (D2) that will work in cooperation with the cablebot and will be able to control the smart irrigation piping installation of the given greenhouse or plot. Device Flow will be able to manage 8 irrigation zones upgradeable to 16. Control zones play a pivotal role in PA (precision agriculture).
According to the stress maps produced by aerial or other means crops are divided into zones for better use of inputs. Areas with different levels of stress need to receive different treatment thus reducing input costs. Development of a quadruped roaming agbot (quadruped: four-legged). Considering that soil sensing is also needed to apply precision agriculture principles with success, the next step is the development of a ground sensor carrier that could roam inside the greenhouse or an open field. The device will be a quadruped suitable for uneven terrain. This device will be called Device Gaia (D3). Finally, a user interface app, code-named 3DS App will be also developed. This web-based application will act as the portal from which growers will receive digested feedback from their smart agriculture system onto their PC, tablet or mobile device [11].
The complete approach will integrate data collected from the cablebot Aeir, with data collected from the Gaia agbot and weather data from satellites to produce an autonomous smart irrigation schedule for any plot in question. The benefits of using the 3DS system can be identified at various levels. The farmer receives an overview of his crop and of the potential dangers that may exist or arise. 3DS is expected also to anticipate and to properly handle phenomena that could damage a crop. The physical presence of the grower in the field is greatly reduced as 3DS automates irrigation and sends real-time information to the grower. The smart irrigation system contributes positively to irrigation water savings [12]. Water doses for the cultivated species in the greenhouse conditions will be determined in the beginning by the plants surroundings area and plants parameters, and will be improved and adapted by the analysis of data collected from the 3DS system.
The core of competitiveness lies in 3DS ability to integrate data from a variety of sources into a single straight forward application that will assist farmers with their day-to-day operations [13]. The area of ground robots still has substantial ground to cover for those technologies to break into the commercial market. To this respect, the research team has already conducted an extensive market analysis on a global scale, and we have identified all the key players in PA that provide hardware and software solutions.
Worldwide concern about food security and environmental conservation is evident presently. Recent technological advancements in IoT, GPS guided aerial and ground vehicles, molecular biology and AI neural networks have spurred a global investment trend towards ag-tech and food-tech, to remedy for those threats that may lie ahead [14]. The overall aim is to introduce cost-effective spectral imaging inside greenhouses [15]. 3DS reduces the initial cost of PA deployment for farmers.
D1 will be the first device to be developed since we will need to also adjust its physical deployment inside the test greenhouse. As soon as D1 becomes operational, D2 will be developed and all the communication protocols between the two devices will be tested. As soon as we establish the connection, work will commence on the control app so that we start to receive some digested information and begin to design its front end. Finally, work will commence on D3. As D3 will be using the same communication protocols (already developed) our effort will be in developing its kinematics and machine vision so that it moves freely inside confined areas (Table 2).

4. Operational Methodology

The cablebot (D1) will collect data and send it to the coordinator/flow valve (D2) system. D2 will process the collected data and instruct accordingly the solenoid valve attached to the zonal irrigation system of a given greenhouse installation or plot. Data will also be automatically uploaded to our server as backup. The agbot (D3) will act as a support device that inspects up-close any stress areas identified by D1 and will conduct soil sensing at random points in the plot/greenhouse (Figure 4). Various other capabilities are to be addressed at a second stage (possibly another research project) (Table 3).
D1 Operation: The operation of D1 is to be controlled by an Arduino Pro Mini which controls the time intervals between data collection and the overall device movement. The whole process will be controlled by an automatic relay to avoid unnecessary waste of energy. A 12-volt operation is to be achieved through a buck converter (Figure 5).

4.1. Cablebot

The movement of the cablebot along the cable is achieved through brushless motors and its speed is 3.5 m/s. The operation of the system is controlled by an Arduino Pro Mini that controls the time intervals between data collection and device movement. The process is controlled by an automatic relay to avoid unnecessary waste of energy. Operation is achieved with a voltage of 12 volts through a buck converter. Data from the built-in sensors is collected via a built-in Arduino NANO. Once the Arduino NANO has successfully taken measurements (via the tx-rx pins), it sends a signal to the Arduino Pro Mini so that the latter puts the device in standby mode.
Communication between D1 and D2 then begins via Xbee technology, which sends encrypted data packets to the tuning/streaming unit. Once the data is successfully transmitted, it is decoded by a node-mcu. The data is then uploaded to our database. The offered vehicle weighs 930 g and can operate properly in temperatures from −5 C to 45 C, for critical elements (batteries, motors, navigation sensors, etc.) includes backup systems to ensure smooth operation even in cases of individual failure (mainly) subsystems. The basic operation control is done by a computer system with a variety of redundancy components. The vehicle will be delivered with a Li-on technology battery with a capacity of 3000 mAh and operating voltage 2S (7.4 V), where in combination with the solar panel it can operate autonomously for at least a month. It should be noted that Li-on battery technology is more durable in solar systems than competing battery technologies such as Lipo (Figure 6).

4.2. Coordinator

KYTION flow (the flow coordinator/valve). Once the data is successfully transmitted, it is decoded by a node-mcu chip. The data is then uploaded to our database. Node-mcu and not ESP has been selected so that the voltage can be easily converted via the Vin pin. The moderator also has an LCD screen for debugging. Irrigation/fertilization decisions are made through thresholds as all data collected is analyzed and combined with data from external sources, such as satellite weather data, etc. All data processing is done locally so that it does not depend on 3G/4G network availability on site. Using this set of weather parameters, namely temperature (T), relative humidity (RH), wind speed (U) and average daily radiation (Rn), the Penman-Monteith equation produces an ETo reference evapotranspiration rate, which can be used to compares water loss from day to day so that it can calculate the amount of water for irrigation. If a potential hazard or critical situation is detected, the end-user receives a notification through the application. The control/user interface tool has been developed using the Node-Red tool. The back end serves as a programming interface and the front end serves as a user interface.

4.3. Sensors

The offered system includes a relative humidity sensor (Relative humidity sensor) with a measuring range of 20% to 95%. The offered vehicle has an RGB camera with 5 mp and 720 p video recording as well as 2x optical zoom for troubleshooting in remote cultivation, this function is compatible with any mobile device or PC with an internet connection. The multispectral RGB camera records the spectral image of plants and assesses their water and nutritional status, as well as potential hazard from pests and diseases. It also has a temperature sensor from 0 C to 50 C, air synthesis sensor (CO 2 VOCs), UVA sunshine sensor (365 ± 10 nm), UVB (330 ± 10 nm) and ground humidity sensor 0% to 70%. Finally, the offered system will have suitable sockets for all the sensors, which have been described. These sockets can be easily removed or added by the operator so that the desired sensors can be used at any time.

4.4. Web Application

The web application of the system displays clearly and in an easy-to-understand way all the measurements that the system receives. Since the app is web-based it is iOS and Android compatible automatically. The user can access real-time measurements and system settings, it is also possible to remotely collaborate and share files with members of a group that the user will select. All data collected by the system can be exported to any farm management software. The application is in cloud format with minimal requirements on hardware equipment for data processing.

5. Data Collection and Transmission

Data shall be collected on vegetables and flowers in a greenhouse installation of the Agriculture Department of University of Patras. The installation is a fully equipment modified arched greenhouse made of high-quality materials. It is the result of scientific design and advanced technology. The frame consists of steel construction elements of different cross-sections according to European specifications. The greenhouse roof is covered with sheets of hard polycarbonate plastic. These sheets are resistant to chemicals, acids, detergents but also salt, sea air and pollution. They are not flammable. They can absorb impact with hail with a diameter of 20 mm and an 80 km/h speed.
The greenhouse is equipped with the following technologies:
  • Meteorological station with embedded specialized instruments and sensors for monitoring and controlling greenhouse micro and macroclimate. Indicatively reported: Datalogger (CR1000X) with two relay analog multiplexer units
  • Sensors, including a silicon type pyranometer (model SPLITE), a Thermopile-type pyranometer (model CMP3), a Photo-synthetically active radiometer (model PAR-LITE), a Pyrgeometer (model CGR3), a temperature and relative humidity probe (model MP101A), a Temperature and relative humidity probe (model S3CO3), a rain gauge (model 52203), an anemometer (model A100K) with threshold sensitivity 0.15 m s 1
  • Analyzer Datalogger Software
  • Photovoltaic panel and accumulators of electricity
Data from the D1 embedded sensors is to be collected through an integrated Arduino NANO. The D1 embedded sensors are:
  • Temperature sensor (Ta_glass in C)
  • Relative humidity sensor (RH_glass in %)
  • Radiation sensor (Sol_glass in Watt/m 2 )
  • PAR sensor (Par_glass mol · s 1 · m 2 )
  • Multispectral camera [16]
In the future, other sensors e.g., CO 2 concentration, air composition and quality et cetera could be installed.
A pilot greenhouse installed at the University of Patras is used, as the main test bed of the proposed technologies. The East-West axis of the greenhouse is designed in such a way that it has an absolute physical resemblance to productive greenhouses. Its dimensions are as follows: width: 9.60 m, length: 12.0 m, gutter height: 4.00 m, ridge height: 6.50 m, area of the greenhouse: 115.2 m 2 .
During the D1 oval movement inside of the greenhouse, all data were continuously recorded at the South and North sides, providing averages every 10 min. Data were stored in a data logger (CR1000X, Measurement and control module, Campell Scientific, Logan, UT, USA) with a relay analog multiplexer. Overall daytime and night-time mean values were also computed from the respective time series, based on the interval between the steep radiation and temperature changes at sunrise and sunset. The two following tables demonstrate the data collected from the pilot D1 installation in the pilot greenhouse (Table 4 and Table 5).

6. Conclusions

IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment [17].
An integrated IoT-based robotic greenhouse control system, a cablebot device, was developed together with a prototype electric valve (coordinator) and the necessary interface app. The operation of the whole system has been initially tested and is now being installed in a greenhouse installation, belonging to the Agriculture Department of the University of Patras. The system can collect a series of crop sensitive data and performs autonomously several tasks such as automated smart irrigation.

7. Future Work

On-site testing with real measurements, data collection, and mapping will be the subject of further study. Taking into account actual data, we will conduct testing of some internal equations. Collecting real data could lead to new ideas and an extension of our system. Realistic future improvement proposals could be: our system to be fully functional in open area environments for low temperatures; a possible movement of the cablebot, as we know that the behavior of the drive yarn varies with temperature; Gaia in case of ice could walk; Could a possible move in the future with tracks provide a solution; The degrees of movement of the robot need improvement for perimeter movement in space; Application of extra technologies for greater energy autonomy. Development of 3DS system for open fields.

Author Contributions

Conceptualization, D.B. & K.-N.P.; Formal analysis, V.T.; Investigation, D.B. and K.-N.P.; Methodology, V.T. and D.T.; Project administration, A.K.; Supervision, D.T.; Writing—original draft, V.T.; Writing—review & editing, D.T. and A.K. All authors have read and agreed to the published version of the manuscript.


The paper was funded in the frame of “Development of Intelligent and Energy-autonomous Greenhouse Using Innovative Technologies to Improve Productivity and Product quality” (IEnGreen) program, MIS:5045455 Smart Specialization Strategy-RIS3.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. The KYTION cablebot.
Figure 1. The KYTION cablebot.
Agronomy 11 00405 g001
Figure 2. The ground roaming agbot.
Figure 2. The ground roaming agbot.
Agronomy 11 00405 g002
Figure 3. The cablebot.
Figure 3. The cablebot.
Agronomy 11 00405 g003
Figure 4. Operational Method of the 3DS System.
Figure 4. Operational Method of the 3DS System.
Agronomy 11 00405 g004
Figure 5. Technologies used in our system.
Figure 5. Technologies used in our system.
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Figure 6. Communication between D1 and D2.
Figure 6. Communication between D1 and D2.
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Table 1. KYTION Aeir comparison with drones and fixed sensors.
Table 1. KYTION Aeir comparison with drones and fixed sensors.
KYTION AeirDroneFixed Sensor
Soil sensing
Sunlight sensing
Rain sensing
Humidity sensing
Temperature sensing
RGB camera/sensor
Spectral camera/sensor
Measurement frequencyUser definedNeeds operatorUser defined
Fertilizer applicationFlow valve
IrrigationFlow valve
Max area coverage4 hectareslargesmall
Table 2. Device functionalities.
Table 2. Device functionalities.
Device 1 (D1), Cablebot(i) Development of functional 3D design software for D1 Cablebot printing
(ii) Design of its functional circuits with all the necessary hardware
(iii) Software programming of all the individual components of the system (Xbee for communication, Raspberry and/or Arduino for control)
(iv) Overall check of the proper operation of the device and its components
Device 2 (D2), Flow valve(i) Development of a functional 3D design software for the solenoid printing
(ii) Design of its functional circuit with all the necessary hardware
(iii) Software programming the individual components of the system (Xbee for communication, Linux SBC for data checking received from the cablebot and uploading them to our server)
(iv) Overall check of the proper operation of the device and its components
Device 3 (D3), Quadruped Agbot(i) Development of a functional 3D design software for the quadruped printing
(ii) Design of its functional circuits with all the necessary hardware
(iii) Programming of all the individual components that make up the system and solving kinematics algorithms for its precise motion
(iv) Overall check of the proper operation of the system and its communication protocols with D1 and D2
Support/Control App(i) Development of a prototype user interface app that feeds the end-user with digested information and data from the coordinator.
(ii) Check and double-check for user-friendliness (easy to follow and understand)
(iii) Improved graphical design
(iv) Add-on functions such as manual override etc.
Table 3. Technical Characteristics of the 3DS System.
Table 3. Technical Characteristics of the 3DS System.
(Cablebot)(Agbot)(Flow Control Valve)
Soil sensingn/a
Sunlight sensingn/a
Rain sensingn/a
Humidity sensingn/a
Temperature sensingn/a
RGB imagesn/a
Spectral sensing/imagesn/a
Measurement frequencyEvery hourUser definedn/a
Sprayingto be determined
Fertilizer application
AutonomySolar-powered1 hourSolar-powered
Obstacle recognitionn/a
Ingress protectionIP67IP67IP67
BatteryLiPo 2400 mAhto be determined
communication protocolXbeeXbeeXbee
Max area coverage4 hectares10 hectares10 hectares
Management Zonesn/an/a8 or 16
Table 4. Mean day-time variations of climatic parameters over the measurement period.
Table 4. Mean day-time variations of climatic parameters over the measurement period.
5 March 202034.0849.332.3051.27284.94530.54230.99446.76
6 March 20202663.3424.9858.91208.17395.84171.18319.48
7 March 202028.1570.6926.4568.26207.27391.22173.17326.04
8 March 202024.376.5822.5777.94166.24298.19166.5293.56
9 March 202020.8969.6520.3868.54129.25238.71131.87228.97
10 March 202022.682.8522.4886.56144.66272.47125.91245.64
11 March 202019.2192.5119.0293.5991.92153.4496.3154.79
12 March 202031.1648.8629.8152.35335.82629.85247.24468.26
13 March 202028.3847.8728.0749.39276.83511.42219.84401.37
14 March 202029.6637.0128.3738.95328613.46243.23460.84
15 March 202028.1841.1427.4740.38277.35520.93215.79391.58
16 March 202028.744.4628.242.35279.45514.62210.4418.34
17 March 20203039.1628.939.68345.32635.6278.56529.15
18 March 202028.1951.0227.0248.19326.32604.69268.5492.75
19 March 202028.7857.3226.8455.91299.2566.95252.03475.54
20 March 202031.9751.7630.1749.26337.58630.09270.83527.22
21 March 202029.1958.8827.8455.32288.39541.08252.26489.47
22 March 202028.8160.2226.9460.97275.01522.12212.22436.47
23 March 202032.6350.3631.3247.56358.99665.77276.46540.23
24 March 202037.2764.9835.4268.83367.51682.22289.22550.03
25 March 202030.1854.7729.1252.5363.36686.81283.95549.46
26 March 202027.6772.4826.1573.55256.14480.68216.86422.63
27 March 202027.6352.8428.0552.2325.96621.44266.63519.54
28 March 202028.9944.8927.5143.98342.19659.81278.95536.74
29 March 202025.943.324.9440.42323.83634.71274.22536.77
30 March 202022.8852.2623.8949.49223.7429.19182.75349.46
31 March 202032.0933.3031.8331.6353.34668.74265.65531.74
1 April 202031.7639.5932.0836.1373.87696.76273.87519.57
2 April 202027.5956.4527.8551.86310.05576.84249.91485.72
3 April 202021.5280.7121.179.4795.36138.979.5130.88
4 April 202030.4449.7929.7448.53387.86743.83274.85548.14
5 April 202025.8761.5225.3160.43254.55474.1185.62365.99
6 April 202027.4559.3927.1659.7277.65511.92205.4409.82
7 April 202028.4259.7928.3657.5306.97574.02230.17432.73
8 April 202029.5346.6229.2344.84403.57757.42281.44541.9
9 April 202030.9942.9630.1138.80380.19706.58287.66555.56
10 April 202029.5646.9628.7241.59319.07596.21260.68493.43
11 April 202029.7947.4428.4446.3367.1688.83263.6499.27
12 April 202031.5351.6630.0649.91395.53734.23275.42539.44
13 April 202031.6348.8330.447.24395.95734.44278.25543.06
14 April 202031.7451.629.9950.43396.89749.66280.10548.55
15 April 202030.6455.8129.1354.11390.45750.83278.33549.57
16 April 202026.2956.9124.5357.42348.78675.2274.42513.91
17 April 202025.9878.4524.6378.99249.56469.63192.74375.78
18 April 202028.2859.626.460.55380.6725.82293.97594.08
19 April 202023.7763.322.3464.36322.17615.24261.14494.47
20 April 202023.4548.6622.4548.31329.52629.69276.42528.42
21 April 202023.8956.1122.5255.31342.04641.4264.46496.45
22 April 202021.7565.3220.3365.37258.96460.58203.52358.35
23 April 202027.4756.6925.0359.71381.52707.02282.82526.26
24 April 202025.4552.1323.2652.91408.54791.58299.65567.63
25 April 202027.8662.0525.6566.11398.53759.72288.75554.5
26 April 20202848.7824.8148.59403.87761.03296.06556.87
27 April 202029.4251.8726.5352.54400.02759.33310.48594.01
28 April 202028.6647.1426.1549.32388.17733.43300.15563.17
29 April 202026.3845.9623.9547.37272.38504.41227.09428.95
30 April 202028.3461.226.1266.21340.09650.87269.33510.95
1 May 202029.6464.327.6167.29373.34710.38289.33550.5
2 May 202029.9264.4827.6869.35366.97698.91282.51534.17
3 May 202028.559.7125.9863.27375.5731.28286.65506.65
4 May 202028.4357.1125.8259.18355.33680.46269.17520.95
5 May 202027.1758.5924.4861.74356.05710.45296.98579.83
6 May 202028.2856.1925.8160.49330.97659.75281.51551.08
7 May 202025.5756.2723.6456.93322.2627.54276.78531.5
8 May 202027.0757.3524.6359.09363.79707.84324.07616.34
9 May 202026.8557.624.0956.67349.96686.09310.61599.69
10 May 202027.0957.5527.1751.12373.83736.34322.79619.63
11 May 202026.2552.8725.4850.08300.98596.185279.56538.35
12 May 202027.6841.8926.4540.49261.85508.76234.48436.69
13 May 202029.0845.7928.2743.59234.97461.19212.01410.27
14 May 202028.8149.8628.4548.36276.09536.35265.42492.15
15 May 202028.6253.8528.2150.68317.51620.54272.07451.68
16 May 202021.2989.221.0782.1499.4189.5684.42112.38
17 May 202020.9276.6620.8972.15108.5203.24102.44166.55
DAY AVERAGE27.8156.2826.5455.82310.32587.64246.46468.77
Table 5. Mean night-time variations of climatic parameters over the measurement period.
Table 5. Mean night-time variations of climatic parameters over the measurement period.
5 March 202016.9575.2615.5183.510000
6 March 202017.0310017.0198.360000
7 March 202017.1397.3317.0694.780000
8 March 202016.8788.6316.9589.090000
9 March 202016.4491.7516.7389.950000
10 March 202017.0299.4217.1699.180000
11 March 202017.0896.6317.2597.30000
12 March 202016.5685.4616.8191.090000
13 March 202016.9587.1117.0892.030000
14 March 202015.2179.1415.4486.130000
15 March 202016.0276.4816.1681.190000
16 March 202015.9978.516.1382.090000
17 March 202015.8984.4415.9793.010000
18 March 202016.4899.5116.7688.490000
19 March 202017.0799.9517.1993.270000
20 March 202017.0310017.1896.440000
21 March 202017.1210017.0599.890000
22 March 202016.9899.9916.9399.990000
23 March 202017.0310017.1999.540000
24 March 202017.1910017.2996.350000
25 March 202017.2810017.361000000
26 March 202017.2110017.191000000
27 March 202016.92100.2171000000
28 March 202017.0810017.1399.530000
29 March 202016.7699.9716.8294.750000
30 March 202016.9410017.0298.280000
31 March 202016.9899.0817.0888.490000
1 April 202017.0398.3817.2488.350000
2 April 202017.1299.4517.2687.770000
3 April 202016.8810016.791000000
4 April 202016.9910017.1895.940000
5 April 202017.0210016.9399.990000
6 April 202016.8310017.0799.660000
7 April 202016.8810017.0498.930000
8 April 202016.901001795.830000
9 April 202015.9583.1116.1565.320000
10 April 202016.8789.7716.9669.260000
11 April 202016.4892.3616.7675.440000
12 April 202016.8896.6516.9880.840000
13 April 202016.9899.7417.16890000
14 April 202017.0199.1217.1384.420000
15 April 202016.9895.1417.2879.470000
16 April 202016.9299.9417.0390.420000
17 April 202016.9299.716.8993.420000
18 April 202017.0310017.1997.720000
19 April 202016.898.2916.8588.710000
20 April 202016.8499.5916.7696.580000
21 April 202016.9896.0717.1285.190000
22 April 202015.0283.8315.3566.610000
23 April 202014.298.2114.4277.640000
24 April 202015.3192.0215.4776.260000
25 April 202016.9610017.1996.480000
26 April 202014.9899.8815.3881.050000
27 April 202016.8810017.081000000
28 April 202015.8196.1115.877.790000
29 April 202016.0272.8816.268.220000
30 April 202018.3272.0218.3267.280000
1 May 202017.4610017.6696.650000
2 May 202017.4199.9817.5298.220000
3 May 202017.4299.7817.6693.630000
4 May 202017.1297.7717.2582.660000
5 May 202017.0899.0817.4491.030000
6 May 202017.1199.2517.3988.580000
7 May 202017.477517.7269.010000
8 May 202017.1198.2317.0894.880000
9 May 202016.9097.2417.0183.40000
10 May 202016.8397.2216.7281.080000
11 May 202017.1492.0917.2175.560000
12 May 202017.9277.5918.1770.590000
13 May 202024.0635.5424.3230.560000
14 May 202018.1898.2718.6391.240000
15 May 202017.9481.5818.0873.630000
16 May 202017.9299.7818.1795.150000
17 May 202017.0610017.1299.950000
NIGHT AVERAGE16.9393.9117.0588.140000
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Thomopoulos, V.; Bitas, D.; Papastavros, K.-N.; Tsipianitis, D.; Kavga, A. Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System. Agronomy 2021, 11, 405.

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Thomopoulos V, Bitas D, Papastavros K-N, Tsipianitis D, Kavga A. Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System. Agronomy. 2021; 11(2):405.

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Thomopoulos, Vasileios, Dionysios Bitas, Kyriakos-Nikos Papastavros, Dimitris Tsipianitis, and Angeliki Kavga. 2021. "Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System" Agronomy 11, no. 2: 405.

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