




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 8(1) (2023), 1-8 

 

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     MULTIDISCIPLINARY SCIENTIFIC RESEARCH 

 
        BJMSR VOL 8 NO 1 (2023)  P-ISSN 2687-850X  E-ISSN 2687-8518 

 
        Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 

                                                                                                                                            

IOT SENSOR TECHNOLOGY AND CLOUD APPLICATION ON 

FARMING PRACTICE: PLANT LIVE DATA MONITOR IN 

AGRICULTURE                 

          
 Tanjea Ane (a)1   Tabatshum Nepa (b)    Mahfuzur Rahman Khan (c)    

 

(a) Assistant Professor, Department of Computer Science and Information Technology, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman 

Agricultural University, Salna, Gazipur-1706, Bangladesh; E-mail: tanjea@bsmrau.edu.bd 
(b) Department of Social Policy, School of History and Social Sciences, Bangor University, Bangor, Gwynedd, North Wales, LL57 2DG, United Kingdom; 

E-mail: niipa.ju35@gmail.com 
(c) Department of Business Administration, Faculty of Bachelor of Business Administration, University of Asia Pacific, Bangladesh, E-mail: 
shaon2028@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 8th August 2023 

Revised: 26th October 2023 
Accepted: 20th November 2023 

Published: 25th November 2023 

 
Keywords: 

 

Internet of Things (IoT), Cloud Application, 
Smart Farming, Mysql Database, Live Data 

Monitor 

 
J.E.L. Classification Codes: 
 

Q110, Q130, Q160  

 

 

  

 
A B S T R A C T 

 
Modern farming practices emphasize cutting-edge technology for plant data monitoring since data 

monitoring enables farmers to promote sustainable farming practices in fieldwork. World agriculture is 

looking at sustainable management practices on agricultural farms. Real-time plant data monitoring 
gets priority in precision farming, making farmers informed plant decisions. The study aims to design a 

sensor and cloud application for farmers’ plants’ live data monitor that provides an opportunity for 

earlier disease and pest detection to control long-term and effective agricultural development and 

provides sensor wireless network support to track plants' natural characteristics and anomalies 

detection. This research investigates sensor applications in farming practices to collect plant live data 

to produce information before making decisions about plants by farmers. The application applied the 

MySQL cloud database to store plants' analogue data; smart devices connect farmers to the plant field 

virtually. The article experiment reveals that remote farmers can direct plants’ current condition to the 
environment. Farmers get support to take immediate actions based on live data behaviour in an 

innovative way; thus, fast response for plants improves production quality and optimizes resource use. 

The study found that live data monitoring with a sensor network and cloud server application provides 

a technology-driven data collection model that efficiently analyzes data interpretation for farming 

practices. 

 
 

© 2023 by the authors. Licensee CRIBFB, U.S.A. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).  

            

 

INTRODUCTION 

Innovative technology has changed conventional farming output into the modern farming industry. Global agriculture has 

experienced farming challenges through traditional agriculture criteria without using advanced smart farming policies. 

Modern farming is being followed by world agriculturists/farmers; therefore, adopting smart farming practices is needed to 

meet the growth demand for agricultural products. Natural ecosystems with environmental disaster threats and crop field 

agriculture are likely to shift towards sustainable agriculture quickly. Industrial revolution technology shows directly an 

agricultural revolution from traditional to smart agriculture with emerging sensor technology used in farming to make it 

sustainable. 

  Food production, supply in the market, user demand, the price set in the market, and service for farmers and end 

users are highly related to agriculture and business, i.e., agribusiness. Its branch significantly employs farming and 

agricultural practices. While agriculture deals with the development of crops, soil, and food production, other cases keep 

agriculture under commercialization, causing no agricultural production value in the supply chain. The food market demands 

to feed seventy per cent of the population by 2050 and needs more consumption demand for increased consumption in the 

future world, according to Ravi and Gopal (2017). Therefore, food demand will surge demanding in future farming 

emphasizing IoT (Internet of things) and IofT (Internet of farm things) that could assist in food production. Internet of Farm 

                                                      
1Corresponding author: ORCID ID: 0000-0003-0329-8626 
© 2023 by the authors. Hosting by CRIBFB. Peer review under the responsibility of CRIBFB, U.S.A.  

https://doi.org/10.46281/bjmsr.v8i1.2129 

 
To cite this article: Ane, T., Nepa, T., & Khan, M. R. (2023). IOT SENSOR TECHNOLOGY AND CLOUD APPLICATION ON FARMING PRACTICE: 

PLANT LIVE DATA MONITOR IN AGRICULTURE. Bangladesh Journal of Multidisciplinary Scientific Research, 8(1), 1-8. 

https://doi.org/10.46281/bjmsr.v8i1.2129 

https://orcid.org/0000-0003-0329-8626
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/bjmsr.v8i1.2129
https://orcid.org/0009-0006-1745-7025
https://orcid.org/0009-0003-6891-0326


Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

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Things (IofT) smart devices are used to monitor field analogue data and data from crop growth zoon. IofT devices are 

technologically used in innovative agricultural farming practices. 

  Besides, the article (Quddus & Kropp, 2020) investigated agricultural farmers living with challenges, especially in 

the lagging regions of Bangladesh, and their farm income, agricultural practice, input patterns, and farmers’ technical 

support survey most people here earn from agricultural production like others country community hence technology priority 

should be given into agricultural practice for lagging regions farmers. Another article (Farooq et al., 2020) surveyed IoT 

technology and technology solutions to meet context quality and quality production demand for agriculture industrialization. 

Sensor wireless networks help farmers collect crop information, and cloud services allow farmers to access field data 

remotely to make crop decisions. Crop production-related climate change and soil moisture issues are addressed (Dhanaraju 

et al., 2022). To improve crop production and reduce the use of fertilizers and pesticides, consider the advancement of IoT 

technology in automatic agricultural operations, such as utilizing communication infrastructure, acquiring data, intelligent 

information systems, decision-making, etc. Jurišić et al. (2021) encompassed frequently used sensors in agriculture 

applications and depicted sensor types according to detection, recording, measuring, and data representation. The application 

of various sensors records the real-time in farm production processes. Sensor technology is operationally efficient, and it 

can accurately detect data factors. Authors claimed that sensor technology made precision agriculture development.  

  Furthermore, Nakhon et al. (2017) examined how technology can solve many traditional farming issues, such as 

manual labour price, improper water management, and environmental change conditions. Proposed smart farming 

techniques using sensor devices can accurately detect humidity, temperature, and water levels in the soil, and explained 

three key components to implement the smart farming model sensor device, gateway, and cloud service. Industrial 

technologies used in agricultural experiment fields are likely to grow merely high-quality production and quantity and 

decrease farm manual labour, i.e., using smart devices like smartphones, sensors, robotic applications, smart irrigation, field 

root data analysis, etc.  

  Technology applications have a significant impact on agricultural production systems. Sensor technology is a 

potential key driver to transform agriculture to digitalization. Sensor application helps farmers prevent crop diseases, assess 

plants’ health, and improve data and information analysis to produce yields. The sensor revolution in technology 

significantly impacts all farming practices and management systems. Sensor technology can assist in environment, soil, and 

plant growing conditions, monitoring trees’ health factors, mapping their biological viability, detecting growing risks, and 

so on (Kayad et al., 2020) discussed sensors application.  

  The above studies examined different technologies and found IoT sensor applications, cloud services, smartphones, 

and robotics transforming agriculture to industrialization. Hence, IoT-based smart devices provide more agricultural benefits 

in farming practices than traditional agricultural production processes for industry. The smart production strategy helps 

farmers make knowledge-based decisions in farming practices. Farmers can be alert about crop insect attacks and natural 

calamities by using devices through notifications. Farmers are becoming smarter in agricultural work. The article (Rubio & 

Más, 2020) says modern farming can manage agricultural goals with objective information emerging from data-driven 

solutions in farming. 

  Also, Klerkxa et al. (2019) picked digital agriculture's social and economic conditions. Agriculture 4.0. It employs 

precision farming and digital agriculture, linking modern digital agricultural practices with farm diversity and transparent 

production in the supply value chain. Big data, the Internet of Things, robotics, sensors, drones, 3D printing, and artificial 

intelligence are many forms of digitalization applied in modern digital agriculture. Kilpatrick and Johns (2003) studied how 

farmers learn farming and business management to follow strategic and tactical changes. Farmers learning change farm 

management successfully, marketing practice makes capability to make them outward looking and focused on people 

connecting extensive networking.  

  Internet technology and communication are applied to farm management in agriculture and modern gardening. The 

U.K. farm business adopts Internet technology for agricultural business. Farmers apply e-commerce technology to launch 

small to medium-sized enterprises (Warren, 2004). In contrast, farmers use internet technology for agricultural 

microbusiness, resulting in agricultural development and human capital-related commercial business. Another study 

examined that agriculturists are highly influenced to apply I.C.T. and IoT technology in farming practices. The modern 

gardening system follows I.C.T. technology to handle information management system (Wang et al., 2010) and hold 

essential guidelines for garden marketing such as planting, seeding, caring, harvesting, maintenance, spraying, and 

monitoring are tracked down using technology. Another article (Tiwari et al., 2016) wrote on CAD (computer-aided 

designing) developed for landscape gardening that creates 2D and 3D gardening models using computer application 

software. Agricultural IoT development for garden planning and flower design management, smart gardening proposed (Jia, 

2021). IoT technology developed machine-based irrigation systems that assist farmers in getting more relevant information 

for better decisions (Ragab et al., 2022) to make agriculture more sustainable. Robotics knowledge is applied to operate 

complex tasks for a human expert. Smartphones with digital features and more powerful computing devices are added to 

farming practices (Pongnumkul et al., 2015). Now, producers can work and run farm businesses using a smartphone. IoT 

and cloud service applications based on cloud technology are powerful and demandable online applications for farming to 

fix traditional agricultural challenges (Awan et al., 2021). IoT services in agriculture require low power, maintenance, and 

installation costs to operate and also provide more robust network connectivity among farming agents, ensuring agricultural 

and industrial zones with large-scale production parameters (Basnet & Bang, 2018). According to Obaideen et al. (2022) 

designed a sensory smart irrigation system, highlighting precision farming with low-cost IoT technology. The technique 

developed a network of wireless sensor nodes used by irrigation systems to sense, compute, and transmit information on 

different essential parameters to control the growth of plants. The authors considered two types of irrigation systems, i.e., 

suspended cycle irrigation systems based on traditional timer controllers and water-on-demand irrigation that sets the 



Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

3 

threshold to meet soil-required moisture. Therefore, the authors explored survey articles related to modern agricultural 

technology that result in IoT sensors and cloud applications that have brought modern farming practices, and embracing 

these technologies illuminates the challenges of increasing production demands. 

  The paper is organized into several sections, including an introduction explaining sensor technology development 

in agriculture, application process, and controlling farm management using modern technology. The section on materials 

and methods describes the model flowchart, sensor application and configuration for plant data collection, and farmers’ 

mobile app configuration. The result and discussion section presents output in the serial monitor of sensor data values, 

cloud-designed live database, and farmers’ mobile notifications. Lastly, the conclusion section shows agricultural field 

research findings, limitations, and future research applications.  

 

MATERIALS AND METHODS 

The proposed article application flowchart is presented in figure1. I defined all IoT sensor devices as configured to retrieve 

data from plant areas. The collected data is in analogue format; hence, information must be processed from analogue data. 

IoT microcontrollers can read analogue data and convert it into readable format. Then, plant data and information are easily 

accessible by field designers. The IoT sensor technology circuit is designed to connect plant field data directly to the 

computing chips of the microcontroller. Retrieve sensor data needs to be sent to the cloud database for storage, so the cloud 

database (MySQL) query is open-source and is preferred to be implemented for our research experiment. The database is 

initially empty because manual data are not kept in the dataset. Only plant live data are stored here. 

Dataset designed for plants’ live data type. If the data type does not fit, a false value will be generated as 

identification in the decision-making stage, i.e., 'Is cloud storage connected or not?'. Microcontroller and cloud storage 

connection established include php script coding. Retrieved plants’ analogue data cannot be performed before any logical 

processing; hence, serial plotters and monitors capture real-time live plant data to check the data connection channel from 

the IoT sensor to the microcontroller.  

Online database storage plants live data, so we designed a cloud server database. Cloud technology database load 

in the online version can hold dynamic data changes in a real-time environment. All data are stored as a sensor data type 

with a mention of time and date. In other cases, data connection failures like internet interruptions or troubleshooting 

problems with missing data can be figured out quickly. Hence, time stamp data needs to be designed on a cloud database. 

The primary key is defined as unique data indexing; therefore, no mismatching data are found on the online cloud database, 

or if data redundancy occurs, it’s quickly possible to remove it from the database. 

An internet connection, i.e., wifi or mobile data, establishes a communication channel between plants’ live data 

and plant designers so that smart mobile applications monitor plants’ live data as long as the database demands. Smartphones 

have the advantage of using Bluetooth systems and pairing capabilities with other devices. Allowing pairing Bluetooth 

devices, plant designers get current mobile notifications in their hands. Live data connected to sensor devices and analogue 

data from plants to receivers is updated until applications automatically stop. Once data retrieval starts, it automatically 

sends data to the server without human interruption until it stops programming code.  

Then, microcontroller commands upload and verify post data, whereas analogue data begins to be retrieved from 

the field, and stored data is automatically visible on the cloud database. Cloud online servers store live plant data and make 

it presentable to plant designers/farmers in the mobile application, which is compatible with pairing. In the disconnection 

of the database or sensor device, power immediately informs the plant designer there is a connection error or missing 

information for Live data retrieval of plants. 

 
 

Figure 1. Data collection and cloud storage workflow 

 



Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

4 

RESULTS  

Experiment Configuration and Result Design 

IoT sensor technology has many types; three types of sensors are applied to field data collection for experiment purposes. 

Soil moisture, temperature, and humidity sensor technology are applied to plant field data and designed on the circuit board 

for data retrieval. 

Arduino microcontroller receives data from sensor devices, and Arduino software writes programming code to 

implement sensor data. Arduino uploads the code, and the serial monitor screen makes data visible in readable data format. 

The agricultural plant data research model requires Arduino software, application library files with IoT sensors, and a 

microcontroller—circuit designed on a Breadboard plate. The microcontroller connects breadboard positive (+) and negative 

(-) connection pins using a few jumper wires. The Arduino board model U.N.O. R3 was designed for experimentation.  

Analogue plant live data is directly recorded from the plant root environment using sensor technology, i.e., soil 

moisture, temperature, and humidity sensors. The moisture sensor needs a VCC pin connected to an Arduino 5V pin, a GND 

pin to an Arduino GND pin, and then an analogue output (A.O.) pin to an Arduino analogue port A2 pin. Temperature and 

humidity sensor values are retrieved using a DHT11 sensor with three pins. The data pin of the sensor is connected to board 

digital port 2—the other two pins, including ground and power, are connected to Arduino pins, respectively. The ESP32 

chip microcontroller supports wifi and Bluetooth connection to the internet and smart mobile phones. Arduino Bluetooth 

Module HC-05 is part of the circuit design to get notifications on mobile phones via Bluetooth. 

Model design local server using xampp, including two modules, Apache and MySQL. Before the application 

triggers, it must install devices—a cloud technology online cloud server designed to store real data from plants. Live data 

are stored on the localhost/phymyadmin cloud server. Data change automatically reflects on cloud storage. The experiment 

model created a database with five variables, i.e., plant's id: primary id, that each plant data has a unique number 

identification. Moisture_data: Retrieve live plant data from the field using a moisture sensor device. Temperature_data: 

holds weather temperature using the dht11 sensor, and humidity_data collects humidity data using the dht11 sensor. The 

Data_record_time variable shows the data collection time and date. For every sample data collection, five variable values 

are inserted into the cloud database from the plant's root zone. Plant I.D. is an auto-generated number, so data redundancy 

can be traced. Sensor values in the database are stored only integer (INT) numbers. Figure 2 shows the experiment 

configuration step by step. Arduino IDE software designed Arduino 2.2.1 version. Library files are required to be installed 

for sensor data reading. D.H.T. sensor library 1.3.7 and <Wire.h>, <WiFi.h>,<HTTPClient.h>,<DHT.h> library files are 

attached with program software. Arduino board port COM3 is selected to upload code, and the result is displayed on the 

serial plotter and monitor. 

 

 
 

Figure 2. Experiment with configuration and set database design 

 

Coding implementation to collect live data from plants is shown in Figure 3. Variables to collect sensor values 

temporarily are initialized as zero values. Three values are declared according to a cloud server's phpmyadm in database 

design. URL holds the Xampp file location with the local computer's IP address. Figure 4 presents how the ESP32 board 

with Arduino IDE uses the post method to send data from Arduino IDE to the cloud site. 



Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

5 

 
Figure 3. Plants' Live data retrieval process through the sensor 

 

 
Figure 4. ESP32 connected and retrieved plants' live data 

 

Figure 5 depicts the ways of PHP scripting code for SQL queries in cloud databases to store live data entry. The 

isset() function checks whether three data variables are retrieved from the plant. The function returns an actual value if the 

variables are filled and are not NULL. Otherwise, the function returns a false value. Coding SQL variable holds insertion 

query into the database while query successfully done results echo (print) denotes record completed. Plant designers have 

the mobile Bluetooth app ‘Arduino Bluetooth Receiver’ to open ArduTooth to pair the H.C. 05 module with a smartphone 

to get plant sensor values as notifications. 

 
Figure 5. Mysql database, Arduino Bluetooth Receiver, and H.C. 05 module plant data presentation 



Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

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DISCUSSIONS 

The experiment output is shown in Figure 6 to upload the Arduino IDE code. The serial monitor in Arduino software displays 

the output result. Three sensor values are retrieved from plant accurate data—live data value changes with time milliseconds, 

accounting for two to five milliseconds in programming code. Sensor A.D.C. value is calculated in output monitor by soil 

moisture sensor that detects plants value min 302 and max 341. Moisture A.D.C.'s high value defines soil as not moisture, 

and A.D.C.'s low value defines the soil as moist as the recorded amount. At the same time, temperature sensors sense weather 

temperatures nearly 30 oc. The cloud server database is designed to store only integer temperature data values in 

phpmyadmin, stores 30 oc and the humidity sensor retrieves 83 per cent values in a particular time. 

 

  Moisture sensor data           Temperature sensor data  Humidity sensor data 

    
 

Figure 6. Sensor data values in the output monitor 

 

This execution phase is crucial to explain since collected plants’ live data were successfully retrieved from plants. 

Zoon now needs to transfer the data’s original value as it is captured to the live database. We designed the MySQL database 

to catch sensor values from fields through communication channels. Cloud database is connected online via the xampp local 

server of the workstation computer. To automatically collect data in the local server, Apache and MySQL module action 

must start, and another function must not occupy the port. Figure 7 summarizes two vertical views of how agricultural live 

data are monitored by farmers or plant designers. Cloud servers hold live databases for plant data and self-directing update 

actions for holding considerable data in the server. Each data has an index as a denoted primary number. Data are captured, 

and that particular date and time are recorded in the database.  

The second part of the following figure shows the farmer's front-end execution output of our research experiment. 

The farmers’ Bluetooth app is paired with the ArduTooth HC 05 module. While farmers are not in the field or nearby, the 

paring feature can access plants’ live data monitoring just using a smart mobile phone device. Farmers may attend to the 

mobile screen and decide what is necessary for plants and when they need more plant care.  

 

     Cloud server for plants lives database                     Notification in farmers' mobile. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 7. Agricultural Live data monitoring 

 

Farmer or plant designers get mobile notifications through IoT and cloud applications with existing online systems. 

The cloud server database collects live sensor data retrieved from root plants. PHP script implements code to transfer sensor 



Ane et al., Bangladesh Journal of Multidisciplinary Scientific Research 8(1) (2023), 1-8 

 

7 

data to farmers’ mobile as a notification. On specified dates and times, plant designers/farmers explore plants’ live data on 

hand, indicating today’s plant data, such as soil moisture value of 332, the temperature at 30 oc, and humidity at 83 per cent. 

 

CONCLUSIONS  

Agricultural practice is changing and highly influenced to equip with sensor technology. Sensor technology with mobile 

applications assists farmers in continuing modern farming practices diversely and makes farmers aware of precision planting 

decisions in a more innovative way. This study implemented IoT sensor applications in agricultural live data collection. 

Live and accurate data from plants' root zone triggers computers or machines to execute necessary plant decisions. Virtually 

monitored data by farmers added new farming practices in the agricultural industry that resulted in modern and digital 

farming practices with technology. Farmers get all the necessary information about environmental effects on plants, which 

is vital in making fast and immediate plant decisions for field planting. This study also covered online cloud databases so 

that farmers can send plants live data or share it with experts for more solutions. Our research has a few limitations. It is 

applied to individual farmers’ practices and field applications. However, in the future, we will expand our work so that the 

application model can be applied to industrial farming practices. Training functions will be arranged if necessary to make 

farmers-friendly use of sensor technology and smart mobile phone applications. 

 

 
Author Contributions: Conceptualization, T.A., T.N. and M.R.K.; Data Curation, T.A.; Methodology, T.A.; Validation, T.A.; Visualization, 
M.M.U.; Formal Analysis, T.A., T.N. and M.R.K.; Investigation, T.A., T.N. and M.R.K.; Resources, T.A.; Writing - Original Draft, T.A., T.N. and 

M.R.K.; Writing - Review & Editing, T.A., T.N. and M.R.K.; Supervision, T.A.; Software, T.A.; Project Administration, T.A.; Funding Acquisition, 

T.A., T.N. and M.R.K. All authors have read and agreed to the published version of the manuscript. 
Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 
Acknowledgement: Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly 
available due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest. 

 

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https://www.researchgate.net/publication/311679804_Computer_Aided_Designing_for_Landscape_Gardening
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http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/

