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Volume 11, Issue 1 available at www.scitecresearch.com/journals/index.php/jisct 1|
Journal of Information Sciences and Computing
Technologies(JISCT) | E-ISSN: 2394-9066
Fertilizer Quality Monitoring System in the Supply Chain based
on Wireless Sensor Networks
Daudi S. Simbeye
Department of Computer Studies, Dar es Salaam Institute of Technology, P. O. Box 2958, Dar es
Salaam, Tanzania
Email: daudi.simbeye@gmail.com; daudi.simbeye@dit.ac.tz
Received: December 29, 2021; Accepted: February 15, 2022; Published: February 26, 2022
_______
Cite this article: Simbeye, D. (2022). Fertilizer Quality Monitoring System in the Supply Chain based on Wireless
Sensor Networks. Journal of Information Sciences and Computing Technologies, 11(1), 1-10. . Retrieved from
http://scitecresearch.com/journals/index.php/jisct/article/view/2118
Abstract
This study
Introduction
A fertilizer is a chemical-based material that is used to improve plant growth and fertility. It may be natural or
manufactured. It is also referred to as any natural or synthetic substance (other than liming materials) applied to
soils or plant tissues to provide one or more plant nutrients required for plant development [1, 2]. Inorganic
fertilizer is one of a limited number of agricultural technologies with enormous promise for increasing the
productivity of impoverished smallholders, allowing them to gain income, amass assets, and put themselves on a
route out of poverty economically [3, 4]. Tanzania, like the rest of Sub-Saharan Africa, is heavily reliant on
imported fertilizer [5].
NPK (nitrogen, phosphorous, and potassium) fertilizers are three-component fertilizers that provide nitrogen,
phosphorous, and potassium [6]. NPK ratings are three digits separated by dashes that describe the chemical content
of fertilizers (e.g., 10-10-10 or 16-4-8). The first number denotes the amount of nitrogen in the product, while the
Scitech
Research
Organisation
Abstract.
Several farmers are reported to be utilizing substandard fertilizer as a result of supply chain concerns
such as inappropriate storage and adulteration by dealers, resulting in soil infertility, low yields, water
pollution, and biodiversity loss. The purpose of this research is to demonstrate the construction of a
wireless sensor network system capable of collecting and analysing data from each stage/point in the
supply chain, as well as communicating status updates and recommendations to important supply chain
partners. The system collected and evaluated data from each stage and point in the supply chain, and it
was able to provide status information and advice to the major supply chain players. This enables the
detection of changes in the quality of fertilizer prior to its delivery to farmers, allowing for the
implementation of appropriate measures. Test results are wirelessly transmitted to the monitoring
software's base station server for analysis, display, and storage through a communication module. The
host server is comprised of an interpretation program that is used to receive, process, and display data
in real time. Users may obtain information from the base station server through their mobile phones. The
remote server of the base station maintains certified fertilizer parameter values for each new batch and
the status of reported fertilizer parameter values for each warehouse and provides the report and
associated advice to the server and users, respectively. On the one hand, users may use predefined
instructions on their mobile phones to seek information about chemical fertilizers and obtain real-time
fertilizer nutrient quality metrics. On the other hand, the system notifies the server and users of the
report and any associated recommendations. The project's results have been positive, and the project's
objective is to aid farmers in making better-informed decisions and boosting agricultural yields via the
use of technology.
Keywords: Fertilizer quality; monitoring system; supply chain; wireless sensor networks.
Journal of Information Sciences and
Computing Technologies(JISCT)
Vol 11,1 – 2022 | E-ISSN: 2394-9066
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Journal of Information Sciences and Computing
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second denotes phosphorous (P2O5) and the third, potassium (K2O). Fertilizer grade or analysis is the weight
percent of available nitrogen (N), phosphate (P2O5), and potash (K2O) in the material, expressed in the order N-
P2O5-K2O. For example, 10-20-10 indicates that the material is 10 percent N, 20 percent P2O5, and 10 percent
K2O by weight. The fertilizer ratio is the weight percent of N-P2O5-K2O and is calculated by dividing the three
numbers by the smallest of the three. Again, using 10-20-10 fertilizer as an example, the ratio is 10/10-20/10-10/10
= 1-2-1. A 23-kilogram bag of fertilizer labeled 16-4-8, for example, contains 3.6 kg of nitrogen (16% of 23 kg), 0.9
kg of phosphorous (40% of 23 kg), and 1.9 kg of potassium (80% of 23 kg).
The movement of fertilizer and related information from production to the end user through the distribution
channels is known as the fertilizer supply chain [7]. Fertilizer products may now be manufactured and supplied to
the ultimate customer thanks to supply chains that extend all the way to the retail level [8]. As a result, a quality
check of fertilizer should be conducted at any point along the retail chain to weed out substandard or counterfeit
fertilizer. Adulteration of fertilizers involves the practice of adding extraneous material to a standard fertilizer to
lower its quality. When a fertilizer contains harmful or deleterious ingredients or unwanted crop or weed seeds in
sufficient quantities to harm the plant when applied according to label directions, its composition differs from that
given on the label, and useless materials such as salt or sand are added to it, it is said to be adulterated [9]. Inorganic
fertilizers are a new technique to provide crops with the nutrients they need while also increasing their output. [10,
11].
Numerous farmers are found to be using substandard fertilizer as a result of supply chain issues such as improper
storage and adulteration by dealers, resulting in soil infertility during cultivation, low yields, water pollution, and
biodiversity loss. Consignments of imported inorganic fertilizer may be contaminated with seeds, soil, and other
plant or animal material, thereby introducing dangerous alien pests and diseases into the country. Contamination
can occur at any point along the supply chain, including the manufacturer, logistics, container loading, and the
container transporting the product to the end user.
In Tanzania, conventional instrumental analysis is used to determine the nutritional quality of NPK chemical
fertilizers, which are then retrieved from the field for laboratory assessment [12]. In general, these procedures need
a large investment in laboratory analytical gear, as well as extensive maintenance, training, and highly experienced
personnel. Because there is no ICT-based instrument to inspect fertilizer quality, private sector executives and
government officials have expressed concern about quality controls executed manually as a result of resource
constraints, such as a scarcity of inspectors. Testing time, equipment investment, complex operation, dependency on
auxiliary equipment, higher testing expenditures, and the inability to use this approach in distant places are all
disadvantages of this approach. For example, the quality of fertilizer can be checked by looking at the business
name, expiration date, and product composition on fertilizer bags. Due to a lack of reliable equipment for
monitoring fertilizer in the warehouse and providing information automatically and on demand, untrustworthy
vendors have used this loophole to offer phony fertilizers to farmers. As a result, real-time assessment of the
nutritional content of NPK chemical fertilizers in the supply chain is becoming more prevalent [13].
The novelty of this study is the development of a wireless sensor network system that can capture and analyze data
from each stage/point in the supply chain, as well as send status updates and suggestions to key supply chain
partners. It should be able to get exact real-time data in order to assist regulatory bodies in taking action when
counterfeit or sub-standard fertilizers are identified. This will enable the detection of changes in fertilizer quality
and the adoption of appropriate steps prior to the fertilizer's being distributed to farmers. The system will be used to
monitor and manage fertilizer quality from the point of import to the point of sale. At the import level, WSNs will
be deployed at the import level to capture and train the system on recognized fertilizer quality values. The same
calibrated WSN will be installed at each level of the supply chain, such as warehouses, to compare the quality of
fertilizer to that captured at the import level. When comparing current data to previously established quality
indicators, the WSN will be able to determine the level of quality decline..
1. Related Works
Numerous research exists that demonstrates how fertilizers contribute to increasing agricultural yields over time
[13–15]. Reference [16] developed an automated, low-cost Internet of Things (IoT)-based fertilizer notification
system for smart agriculture. By developing a unique Nitrogen-Phosphorus-Potassium (NPK) sensor with a Light
Dependent Resistor (LDR) and Light Emitting Diodes, the article demonstrated an IoT-based system (LED).
Colorimetric analysis was utilized to monitor and assess the nutrients in the soil. However, this study only looked at
fertilizer data from a few areas of farming and didn't take the supply chain into account.
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A mobile equipment system has been studied for measuring soil fertility parameters from a field vehicle. The
reference [17] developed a wireless sensor network-based prototype tractor-mounted field monitoring system for
measuring soil nitrate levels in fields. The system was evaluated in the laboratory and in the field. It consists of a
soil sampler, an extraction unit, a flow cell, and a controller. Initially, the soil sampler was equipped with a chain
saw blade and a belt conveyor to collect and transport samples of known volume and density to the extraction and
analysis equipment. However, during field testing, various mechanical and electrical issues were discovered, such
as blockage of the extractor outlet with plant waste, which led to unacceptable levels of noise in the electrode
signal. Additionally, an automated field monitoring system that has the ability to function as a real-time soil nitrate
analyzer was recently enhanced with the addition of an automated sampler that offers exact mass estimations of the
sample. On the other hand, mobile sensing of soil chemical characteristics by reflectance spectroscopy appears to be
less promising. Some positive results have been reported, but there are still problems with calibration and accuracy
that haven't been solved.
To address the aforementioned issues, an enhanced organic fertilizer mixer based on IoT technology was created
that is capable of remotely monitoring fertilizer production and providing updates and notifications to employees on
when to add additional material to the mixture [18]. In [19], an overview of soil macronutrient sensing for precision
agriculture was given. Reference [20] used a color sensor to assess the soil pH and nutrient levels, namely the
presence of nitrogen, phosphate, and potassium. However, the method did not inform farmers of the type of
fertilizer to use for each nutrient.
A recent research study [21] described the construction of a website-based traceability information system for the
supply chain of subsidized fertilizer. A manufacturer may identify product lots and their links to batches of raw
materials, processes, and product delivery using the traceability system. However, the system is missing
mechanisms for measuring and monitoring fertilizer quality across the supply chain. As a result, addressing the
issue of evaluating the quality of fertilizer along the supply chain remains crucial.
2. Materials and Methods
3.1 Hardware structure design scheme
As illustrated in Figure 1, the NPK and pH nutrient sensors are mounted to the panels of the data acquisition device
with an aluminium enclosure. A signal conditioning circuit is included in the NPK and pH sensors to process the
output signal so that it is acceptable for the next stage of operation. It is made up of an analogue-to-digital converter
(ADC) that is connected to a linear amplifier. The signal conditioning circuit performs signal amplification
(opamp), filtering, protection (Zener & photo isolation), linearization, and error compensation. High current and
high voltage are protected from causing harm to the circuit's key components. The cable connects the NPK and pH
sensors to the data acquisition device. The data acquisition device is composed of a microcontroller, a
communication device, a liquid crystal display (LCD) for displaying measured data in real time, and various
electronic components. The control circuit is enclosed in a waterproof aluminum casing.
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100-Liquid crystal display (LCD); 102-Fertilizer nutrient detector; 104-Cable; 106-Signal conditioning unit
Figure 1: Fertilizer nutrient quality detector
This instrument can measure the nitrogen, phosphorus, potassium, and pH levels of chemical fertilizers. The
recording time and the test sample are automatically saved with the display feature. The LCD panel displays the
stored data (test sample, pH, and different NPK nutrient results). The data can be transferred to remote base station
monitoring software for analysis, storage, and real-time display. The interpretation program may receive data from
monitoring software, evaluate it, and provide a report to the server and users with suggestions. Experts and
regulatory agencies can specify fertilizer quality indicators like macronutrient composition parameter values and pH
with reference to the applicable national standards.
NPK and pH, physical parameters, and fertilizer identity information (batch number, manufacturing date) are all
collected by a data acquisition device in the system. Secondly, the analysis and interpretation component deals with
receiving, processing, and displaying data in real time. Lastly, a communication module delivers data from sensors
and RFID readers to a remote server for analysis, display, and storage. This system's base station remote server
stores certified fertilizer parameter values for each new batch and fertilizer parameter status reports for each
warehouse. Sensor node data is collected and processed by the base station remote server. The data is then stored,
reported on, and displayed on a real-time basis. Monitoring software provides data to the base station remote server,
which processes it and delivers the report and relevant suggestions to the server and users. Chemical fertilizer data
may be requested via mobile phones using predefined instructions and received in real time while the system also
transmits the report and relevant suggestions to the server and users. This is a two-pronged approach.
Network
NPK sensor Microcontroll
er unit
Signal conditioning
circuit Communicati
on module
LCD display
Battery
Message
Delivery
Server
Monitoring software
base station
RF ID
sensor
Figure 2: Block diagram of NPK fertilizer nutrient quality detector
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As seen in Figure 2, the control circuit consists of an RF ID sensor, an NPK sensor, and a pH sensor connected to a
signal conditioning circuit and a microcontroller. The system's data acquisition and processing are performed by an
ATmega16L microcontroller. The ATmega16 features an advanced RISC architecture, 113 instructions, 32 general-
purpose registers, and performance up to 16 MIPS at 16 MHz. Other features include two-cycle hardware
multipliers, an 8-channel 10-bit Analog to Digital Converter (ADC), 32 programmable Input/Output (I/O) ports, a
16KB system programmable watchdog timer with an independent on-chip oscillator, power on reset, and
programmable power failure detection. The microcontroller chip then generates control signals in response to a
comparison with standard parameters in order to show measured data in real time on the LCD screen and transmit
measured data to the wireless network through the communication module. The produced data and control signals
are pooled and transferred through WiFi to a gateway linked to the host computer's base station. Data and control
signals generated are aggregated and transmitted through the WiFi network to the gateway, which is connected to
the host computer base station. The system is powered by five 5V DC batteries.
The gateway accepts command packets, preprocesses and analyzes data from sensor nodes, and then transmits it to
the host computer base station. The gateway communicates with the base station through a WiFi connection. The
monitoring software is placed on the host computer's base station and is used to show results and deliver brief
messages to users. Users can obtain measured data by installing a mobile application on their smart phones. In light
of the communication requirements between the host computer and its owners, utilizing the GSM network not only
saves money, but also increases the communication range and area. The host computer base station is comprised of
interpretation software that reads data from monitoring software, analyzes it, and transmits the report and associated
recommendations to the server and users. The data is saved in a database for future usage and forecasting, is
presented in real time, and may be emailed to users. The base station remote server stores certified fertilizer
parameter values for every new batch and reports the status of the fertilizer parameter values for every warehouse
and sends the report and related suggestions to the server and users, respectively. Users, on the other hand, can
request information about chemical fertilizers from their mobile phones by using predefined commands and receive
fertilizer nutrient quality parameters in real time. On the other hand, the system sends the report and related
suggestions to the server and users.
One of the most interesting aspects of this system is that its gadgets may operate without any human or human-
computer interaction. Wi-Fi, and low-powered long-range radio modules have all contributed to the development of
new systems that can forecast and monitor a wide variety of factors. Sending and receiving data via a network is
done by the radio module, which has a unique numeric identifier, or IP address.
3.2 Software system design
This project's embedded software aimed to create a simple, user-friendly interface for interfacing with the main
unit. The program was separated into various functions (subroutines) according to the tasks throughout the
development process to reuse code and therefore make the program resilient and fast to run. Furthermore, the source
code was documented to facilitate future revisions and upgrades. After finalizing the source codes, they were
compiled and debugged to eliminate any potential mistakes. The ability of the user to engage with the fertilizer
quality monitoring system is a key feature. We opted to employ Wi-Fi between the mobile application and the main
printed circuit board (PCB).
The ESP8266 Wi-Fi module was utilized to link the PCB wirelessly. The ESP8266 Wi-Fi Module is a self-
contained SOC with an integrated TCP/IP protocol stack that can connect any microcontroller to a Wi-Fi network.
The ESP8266 may either host an application or offload full Wi-Fi networking capabilities to another CPU. If both
devices are linked to the same wireless network, this Wi-Fi module may interact with them over Wi-Fi. This was
performed using the Wi-Fi module, which acts as a wireless access point for other devices or connects to another
access point by running its own web server. The module was designed to serve as a connection point for other
devices. In this mode, the user may connect their device to the access point and navigate to an IP address where
they can set the name of the wireless network to which the system should be connected as well as the password for
that network. Following this stage, the Wi-Fi module will again create a server, but this time it will be on the
wireless network that the user has selected.
Figure 3 shows the graphical user interface (GUI) which monitors and transfers the real-time data to an Android-
based smartphone via the internet. Additionally, the GUI stores real-time data in an excel sheet with the date and
time for further analysis. The data displayed in the GUI in numerical and graphical form helps in quick
understanding and decision-making.
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Figure 3: Monitored data of NPK from graphical user interface
The sensitive data handled by the software system was considered in our design. These data pieces are any user
information and any private network credentials used by the wireless module on the PCB to connect to the local
private network in our system. Encryption using cipher-block chaining and PKCS5 padding is used in the
implementation. Encryption and decryption of files are performed by applying a cipher to the IO streams. When
reading or writing encrypted files, the static methods of the Crypto class can be utilized.
3. Results and Discussion
Data from each stage/point in the supply chain was collected and analyzed, and the system was able to transmit
status information and advice to the primary supply chain participants. This allows for the identification of changes
in the quality of fertilizer before it is delivered to the farmers, so that relevant steps may be implemented. The
sensor nodes were equipped with NPK and pH sensors to monitor nitrogen, phosphorus, potassium, and pH,
respectively. The system was validated by placing two sensor nodes in the wholesale distributor's warehouse and
two sensor nodes in the retail agro-warehouse.
Sample NPK fertilizer bags were tagged with RF ID and monitored by the system for a 24-hour period. Two
months later, the same bags were measured by the system at the retail store. Real-time data collection and analysis
of fertilizer quality factors like NPK and pH were used to compare the findings. The findings are quite encouraging
and can be used as a diagnostic tool or to identify temporal patterns. After analyzing the trial data, it was
determined that the amounts of macronutrients nitrogen, phosphorus, and potassium, as well as soil pH, varied
consistently but not significantly.
Figure 4 shows the wide range of nitrogen levels found in fertilizer sample bags. In Figure 5, it can be shown that
the average phosphorus content varied between 28.0 and 36.0 mg/kg. Potassium monitoring data is shown in Figure
6. Potassium levels were found to be stable on average, ranging between 24.0 and 26.0 mg/kg. As seen in Figure 7,
the system was examined for pH responsiveness and long-term stability. Fertilizer sample bags were found to be
somewhat alkaline, ranging between 7.4 and 8. The pH of the studied solution is typically steady throughout time,
and no variations were observed.
Determining the nitrogen, phosphorus, potassium, and pH contents of a presented technology can provide insight
into how well it performs in relation to its intended usage. A nutrient analysis determines the average nutrient
concentration (mg/L) for all nutrients examined. In an agricultural setting, understanding the quality of fertilizer
nutrients can assist farmers in avoiding the purchase of counterfeit fertilizer, hence minimizing loss.
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Nitrogen
(mg/kg)
Phosphorus
(mg/kg)
Potassium
(mg/kg)
Time
N
P
K
Figure 4: Monitored data of nitrogen
Nitrogen
(mg/kg)
Phosphorus
(mg/kg)
Potassium
(mg/kg)
Time
N
P
K
N
P
N
Figure 5: Monitored data of phosphorus
Nitrogen
(mg/kg)
Phosphorus
(mg/kg)
Potassium
(mg/kg)
Time
N
P
K
N
P
N
N
P
K
Figure 6: Monitored data of potassium
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Time
1 2 3 4
Figure 7: Monitored data of pH
4. Conclusions
The presented system can collect and analyze data at each stage/point in the supply chain, as well as communicate
status updates and recommendations to critical supply chain partners. It can obtain precise real-time data to aid
regulatory authorities in acting when counterfeit or substandard fertilizers are discovered. This system continually
monitored and reported on the fertilizer's NPK and pH readings. Test results are sent to the monitoring software
base station server wirelessly through a communication module for analysis, presentation, and storage. The host
server consists of interpretation software for receiving data, processing, and real-time display. Users can access the
information from the base station server through their mobile phones. The base station remote server stores certified
fertilizer parameter values for every new batch and reports the status of the fertilizer parameter values for every
warehouse and sends the report and related suggestions to the server and users, respectively. Users, on the other
hand, can request information about chemical fertilizers from their mobile phones by using predefined commands
and receive fertilizer nutrient quality parameters in real time. On the other hand, the system sends the report and
related suggestions to the server and users. The numerous test findings given provide a concise description of the
implemented system's functionality. We conclude that the system's and device's overall functionality is satisfactory.
The project's results are favorable and are aimed at helping farmers make more informed decisions and increase
agricultural yields via the use of technology. The study's findings indicate that monitoring fertilizer quality across
the supply chain requires an integrated approach to fertilizer distribution management. Additional training is
necessary for personnel testing of fertilizer at each stage/point in the supply chain. It is vital that farmers and local
agro-merchants have access to the tested system. This requires adequate training and the provision of the equipment
necessary to conduct such tests. Future system enhancements will necessitate system expansion through the
integration of other sensors and further development of the NPK sensor.
Conflict of Interest
The author declares that he is unaware of any conflicting financial interests or personal ties that would appear to
have influenced the work described in this publication.
Acknowledgements
The author would like to thank members of the Computer Studies department at Dar es Salaam Institute of
Technology for their expertise and technical assistance.
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Authors’ Biography
Daudi S. Simbeye, Ph.D. is a Senior Lecturer in the Department of Computer Studies
at Tanzania's Dar es Salaam Institute of Technology.
He earned a B.E. in electronics and microelectronics from Moscow Power Engineering
Institute (Technical University) in 2006, an M.E. in design and technology in electrical
engineering from Southern Federal University-Taganrog Institute of Technology in
2008, and a Ph.D. in light industry technology and engineering from Tianjin University
of Science and Technology in China in 2014. He worked as an Assistant Lecturer in the
department of computer studies at the Dar es Salaam Institute of Technology in
Tanzania from 2008 until 2014. From 2015 until 2018, he worked as a Lecturer in the Dar es Salaam Institute of
Technology's department of computer studies. He has been a Senior Lecturer in the computer studies department at
Dar es Salaam Institute of Technology, Tanzania, since 2019.
He devotes a great deal of time to teaching, research, innovation, and consulting. He is a registered professional
engineer in Electrical and Electronics with the Engineers Registration Board (ERB) with the registration number
PE4668.
Intelligent technologies, embedded systems, and wireless sensor networks are among his research interests.
