IoT Applications in Agriculture and Environment: A Systematic Review Based on Bibliometric Study in West Africa

Abstract

The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability.

1. Introduction

Digital agriculture is defined as the modern application of information and communication technologies (ICTs) in agriculture to solve food insecurity problems [1,2]. As the Food and Agriculture Organization of the United Nations (FAO) report on food insecurity and urbanization points out, the rate of urbanization is expanding rapidly. By 2050, almost 7 out of 10 people are expected to be living in cities, leading to changes in agri-food systems across the rural–urban continuum [3,4]. This situation raises the challenge of how to increase the agricultural yields for an ever-growing population and to mitigate climate change issues [5]. To address this challenge, countries need to move towards precision agriculture. At the same time, agricultural systems constitute the main source of environmental problems in general and air pollution in particular. These problems induce a global health crisis [6,7]. Indeed, ambient air pollution constitutes a relevant common health disorder, with models and monitoring data indicating a tremendous concentration of pollutants [8]. In order to raise awareness and engage in dialogue with stakeholders on the consequences of air pollution, pollutant levels must be measured, and the data collected and processed must be accessible. Traditionally, data are collected using high-cost solutions that are not really accessible for low- and middle-income countries like most African countries. The development of emerging technologies came to break down these rules by introducing low-cost air quality sensors, a novel and cheaper approach to measuring pollutants.

Recent technological advances offer opportunities for Africa to monitor and protect the environment, as well as the overall planetary health, and produce enough food to keep pace with a growing population. Healthier worldwide environments could prevent almost one quarter of the global burden of disease [9]. Many digital technologies are proposed to fill these requirements. Nowadays, IoT, Data Science, Big Data and Artificial Intelligence are the most proposed to assume the trade-off between raising farm productivity and maintaining healthier environments [10]. While IoT is the most used technology to collect and record agricultural and environment data, the others are mainly useful for data analysis and prediction [11]. IoT is defined as an interconnection of physical devices, vehicles, appliances and other physical things that are enclosed with sensors, software and network connectivity, which allows them to gather and share data [12,13]. It presents several advantages, including real-time access to information, improved monitoring using sensors and machine-to-machine communication. IoT technology has transformed the agricultural and environmental industry by furnishing producers with the real-time information on crop state, soil moisture, weather conditions and pollutants [14]. This information can be used to enhance crop yields, diminish waste, enhance sustainable development [15] and inform environmental stakeholders, improving environmental conditions in Africa in general and West Africa in particular [16,17].

IoT is in its early stage in West Africa, but many works are in progress or already performed concerning its application in both the agriculture and environment sectors. However, there is a lack of scientific review studies simultaneously in both sectors. To the best of our knowledge, none of the existing papers perform a comprehensive systematic review on IoT case studies in agriculture and environment in the West African region. This document aims to fill this gap.

More specifically, our goal is to investigate existing works and extract relevant insights through both a quantitative study (most involved countries and universities, works production over time, etc.) and a qualitative study (use cases, IoT communications technologies used, types/costs of sensors, measured parameters, additional technologies used). Moreover, we identify the weaknesses of the existing solutions and highlight the challenges that should be addressed by future research. The research questions we try to answer are the following:

RQ1: What is the mapping of research on IoT applications in agriculture and environment in West Africa?

RQ2: What are the applications of IoT in agriculture and environment in West Africa?

RQ3: What kind of sensors/technologies are being used?

RQ4: What challenges are hindering IoT adoption in West Africa?

The quantitative analysis uses 53 articles as an input. On the other hand, the qualitative study is proposed based on 20 papers, which were selected (from the 53) given their relevance in terms of applicability in the West African region and with a focus on low deployment cost.

The remainder of the paper is structured as follows. After the introduction (Section 1), related works (Section 2), materials and methods (Section 3) are presented, followed by results (Section 4) and discussion (Section 5). The paper is completed by the conclusion (Section 6).

2. Related Works

There is a continuous research flow relative to IoT utilization in agriculture and environment. It is then necessary to map these fields and give an overview of what has been accomplished so far. This section presents a non-exhaustive list of works that carried out a systematic review of IoT applications in agriculture or environment, in general, or any of their subfields.

2.1. IoT Applications in Agriculture

In 2021, a bibliometric study was carried out on a sample spanning three decades of research, to give an overview of the use of wireless sensor networks in general in agriculture [18]. Their detailed analysis showed that wireless sensor networks are a key element of precision agriculture. The literature on wireless sensor networks (WSNs) revolves around three primary thematic areas, delving into various technologies such as IoT, Artificial Intelligence, unmanned aerial vehicles, RFID, and cloud computing. The interconnections among these key topics underscore the need for deploying WSNs in agriculture and their potential to optimize agri-food processes. Consequently, concerted efforts from scientists, producers and specialists are imperative to enhance the inter-workability of WSN-based agricultural systems and formulate pertinent, adaptive practices and measures. In addition, a bibliometric study of intelligent agricultural environments was performed [19]. Through the appropriate advancement of technologies, the implementation of intelligent systems can facilitate the application of these technologies in the agricultural domain, benefiting, for example, small-scale banana producers by enhancing agricultural efficiency. This, in turn, leads to numerous economic and environmental advantages. Moreover, a bibliometric analysis and review of IoT-based messaging protocols for aquaculture applications was carried out [20]. The results of their work show the scope of research in this area and highlight that protocols such as Hypertext Transmission Protocol (HTTP), Constrained Application Protocol (CoAP), Message Queue Telemetry Protocol (MQTT), MQTT for Sensor Networks (MQTT-SN) and Advanced Message Queuing Protocol (AMQP) are employed for information transfer. These messaging protocols need to consider diverse elements such as the sensing performance limit, storage capability and power usage. In 2023, the authors of the article [21] performed a survey including a bibliometric study on digital agriculture in five West African countries. While the scope of their survey is directly connected to the one of this article, their work did not target all countries of West Africa. It also only focused on digital agriculture, not taking into account environmental aspects and IoT-related aspects. Finally, a review of the state of the art on remote sensing for aquaculture is presented in [22], with emphasis on different aspects: side location, facility mapping, market proximity analysis and roadway infrastructure, epizootic mitigation, meteorological event and flood early warning, environmental pollution monitoring and aquatic ecosystem impact. These last two articles will also be considered in the qualitative analysis and more specifically in Table 3.

2.2. IoT Applications in Environment

In 2020, a systematic review was performed on making use of IoT to monitor air quality in indoor environments [23]. They have shown that 70%, 65% and 27.5% of research concentrates on thermal comfort parameter supervision, CO2 and particle levels, respectively. Among these works, 37.5% of the systems use Arduino controllers, while 35% use Raspberry Pi controllers. Solely 22.5% of the works mention details about field calibration procedures before realization, and 72.5% of the works propose methods for efficient energy utilization. In 2023, a systematic review focused on the use of IoT for assessing and communicating indoor environmental quality (IEQ) in buildings [24]. This article focuses on the link between IoT and IEQ, by presenting modern methods for monitoring the built environment. The authors find that the main objective of using IoT inside buildings is to diminish energy requirements. Machine learning methods serve this purpose in addition to learning regarding resident comportment inside buildings, focusing on thermal comfort. They also highlight the relevance to conceive low-cost detection devices with a learning approach. Finally, they stress that IoT sensors are required to help improve human comfort and well-being.

As highlighted earlier, the goal of this article is to fill an important gap in the literature: the absence of a comprehensive literature review dealing with the application of IoT to agriculture and environment, specifically for West Africa. This paper will furthermore help to situate West Africa’s progress in this area in relation with the international level. Finally, the current article will give some insights into the challenges to be addressed by future works for IoT adoption in agriculture and environment in the West African context.

3. Materials and Methods

In this section, we present the used materials and the adopted methodology to perform the analysis.

3.1. Materials

The main tools used to conduct bibliometric analysis are the RStudio software, the R programming language and the bibliometrix package. RStudio is an Integrated Development Environment (IDE) that constitutes a collection of tools developed to help researchers and practitioners to be more productive with the R programming language [25]. This software incorporates a console, syntax-highlighting editor that allows for direct code completion and contains tools for making plot, history, debugging and workspace handling. It is accessible in open source and marketable versions. Bibliometrix is an R instrument used to analyse the science mapping [26]. It furnishes scientists with a coherent and standardized collection of quantitative indicators, which also assist qualitative deductions and insights. It accomplishes a full set of literature information analysis and allows us to have a graphical view of findings. Also, it helps R users to import a bibliography database from research databases such as Scopus, Lens.org, Cochrane, Web of Science, Dimensions and PubMed, saved either as a Bibtex (.bib) or Text file (.txt). Bibliometrix is sometimes used with the biblioshiny application, which provides a web interface for bibliometrix and helps noncoders to easily use the main features of bibliometrix.

3.2. Methods

The methodology of our study is presented in two steps: the quantitative analysis and the qualitative review. This subsection presents the PRISMA method used for quantitative analysis.

The procedure developed to gather data on the topic consists of extracting information from the Web of Science (WoS) Core Collection database. The research was based on the keywords of publications, titles and abstracts. To gather an efficient collected database, synonyms of keywords selected on our topic were identified and inserted in the search string. The search string obtained is presented as follows: (Agriculture OR Farming OR Environment OR Livestock OR Crop OR “air pollution” OR “Forest Fire” OR Atmosphere OR fish) AND (IoT OR WSN OR “Internet of Things” OR “Wireless Sensor Network” OR sensor OR LoRA) AND (“West Africa” OR “Burkina Faso” OR “Cape Verde” OR “Ivory Coast” OR “Côte d’Ivoire” OR Gambia OR Ghana OR Guinea OR Guinea-Bissau OR Liberia OR Mali OR Mauritania OR Niger OR Nigeria OR Senegal OR “Sierra Leone” OR Togo OR Benin). The country name “Ivory Coast” was also translated in French “Côte d’Ivoire” as this country is known to publish papers with its name appearing in French. It is relevant to note that there was no restriction of time period for the study. A total of 226 documents were recorded between 1996 and 2023 and extracted from the WoS database on 14 August 2023. This data has been filtered using the PRISMA method as presented in Figure 1. The recorded database is accessible using the link after logging in with a WoS account: https://www.webofscience.com/wos/woscc/summary/fa1ed2dd-437a-4981-b034-3ddb92e6696f-ed267ada/relevance/1 (accessed on 14 August 2023). As a disclaimer, we should mention that we only extracted information from the WoS database. Including other databases such as Scopus, IEEE Xplore or Google Scholar could have enlarged the number of documents considered for the study.

Figure 1. Bibliometric study process using PRISMA method.

The PRISMA method includes 3 processing steps: the identification process, the screening process and the inclusion process. It consists of filtering the database to identify the relevant papers. First, 1 duplicate paper was excluded in the identification process. After that, in the screening stage, 94 papers were excluded based on their titles, and 59 papers were excluded based on their abstracts. The papers whose titles and abstracts are only related to agriculture, environment or IoT are not relevant for our study. In other words, the papers maintained in the database are related to an IoT application in agriculture or in environment. The screening was pursued by selecting 72 relevant papers by reading their abstracts. Among them, paper abstracts that present applications of IoT in biology or human health were excluded. One paper was not retrieved. The papers’ full access screening led to 18 excluded papers. Then, in the inclusion stage a total of 53 papers were selected for further analysis. In the following section, the 53 papers selected following the PRISMA method are exploited for the bibliometric study, which constitutes the quantitative analysis. It is followed by the qualitative analysis, after performing a further selection on papers that present low-cost solutions for West Africa.

4. Results

4.1. Quantitative Analysis: Bibliometric Study

Bibliometrics consists of studying academic works using statistics to present publishing trends and to indicate links between published materials. It constitutes a computer-assisted scientific review procedure which helps to identify the key research or authors and their relationship, taking into account all documents based on a given topic. The relevant trends about the database content remaining after the PRISMA filtering method are analysed in the following in this subsection.

4.1.1. Database Main Information

The database contains 53 documents, of which 69.92% are journal papers, while the remaining are conference proceedings, and they have been published in 44 sources. These documents on average have 6 years of age, implying that this topic is relatively recent in West African countries. The annual growth rate is 5.27%. Among 277 authors who wrote these documents, the international co-authorships rate, referring to the collaboration between researchers from different countries in writing papers related to this topic, is about 56.6%. This means that there are more papers that resulted from an international collaboration than from a local one. One can note the lack of single-authored documents.

4.1.2. Annual Scientific Production

Figure 2 shows the annual scientific production and its evolution. From 1996 to 2010, at most one paper was annually published. From 2010 to 2013, a varying paper number between 0 and 4 can be seen. The paper number published per year increases significantly starting from 2014, where it varies between 1 and 8 papers per year. Moreover, the effective growth becomes very noticeable during the last five years, with a peak in 2020.

Figure 2. Annual scientific publications.

4.1.3. Most Relevant Sources and Locally Cited Sources

The most relevant sources refer to the journals that recorded the highest number of papers in the field. Figure 3 indicates the top 10 most relevant sources. It is observed that the MDPI Remote Sensing journal ranks first, with four published documents.

Figure 3. Most relevant sources.

The locally cited source measures the number of times a document published in a collection (in particular a source) has been cited by documents of the same collection (same source). Figure 4 shows the top 10 most locally cited sources. The Remote Sensing of Environment journal ranks first, with 125 citations, which is the sum of the citations of all of the documents from this journal in the database of the 53 documents. It is followed by MDPI’s Remote Sensing open access journal, with 64 published documents. Remote Sensing of Environment is a remote sensing journal founded by Elsevier, Amsterdam, Netherlands in 1969.

Figure 4. Most locally cited sources.

4.1.4. Most Relevant Authors and Most Locally Cited Authors

Figure 5 exhibits the top 10 relevant authors. The author’s relevance is determined on the basis of his or her work degree or interest on the subject, i.e., the number of published papers. It is noted that the author Diallo Moussa, from the Polytechnic Institute of Cheikh Anta Diop University in Senegal, published three documents, while the remaining authors published two documents or one. Table 1 shows the most relevant authors’ full names.

Figure 5. Most relevant authors.

Table 1. Full names of most relevant authors.

Authors	Full Names	Affiliation	City	Country
Diallo M	Diallo, Moussa	Cheikh Anta Diop University	Dakar	Senegal
Awokola B	Awokola Babatunde	Liverp Sch of Trop Med	Liverpool	UK
Erhart A	Erhart, Annette	Lond Sch of Hyg and Trop Med	London	UK
Gessner U	Gessner, Ursula	German Aerospace Center	Wessling	Germany
Gueye B	Gueye, Bamba	Cheikh Anta Diop University	Dakar	Senegal
Kuenzer C	Kuenzer, Claudia	German Aerospace Center	Wessling	Germany
Malo S	Malo, Sadouanouan	Nazi BONI University	Bobo Dioulasso	Burkina Faso
Okello G	Okello, Gabriel	University of Cambridge	Cambridge	UK
Raheja G	Raheja, Garima	Columbia University	New York	USA
Semple S	Semple, Sean	University of Stirling	Scotland	UK

Figure 6 presents the top 10 authors whose documents have been locally most cited. The local citations measure how many times an author in the database has been mentioned by the documents included in the database. Table 2 shows the most locally cited authors’ full names.

Figure 6. Most locally cited authors.

Table 2. Full names of most locally cited authors.

Authors	Full Names	Affiliation	City	Countries
DECH S	Dech, Stefan	German Aerospace Center	Wessling	Germany
ESCH T	Esch, Thomas	German Aerospace Center	Wessling	Germany
GESSNER U	Gessner, Ursula	German Aerospace Center	Wessling	Germany
KUENZER C	Kuenzer, Claudia	German Aerospace Center	Wessling	Germany
Machwitz M	Machwitz, Miriam	Lux. Inst. of Sci. and Tech.	Belvaux	Luxembourg
Naeimi V	Naeimi, Vahid	Vien. Univ. of Tech.	Vienna	Austria
Raheja G	Raheja, Garima	Columbia University	New York	USA
Tillack A	Tillack, Adina	German Aerospace Center	Wessling	Germany
Appoh Eke	Appoh, Eke	Ghana Env. Prot. Agen.	Accra	Ghana
Gbedjangni EK	Gbedjangni, Eric	University of Lome	Lome	Togo

4.1.5. Most Relevant Affiliations

Figure 7 displays the top 10 relevant affiliations. It is noticed that the University of Abomey-Calavi (Benin) leads the list with nine publications. It is followed by the Nigerian Oluwafemi Awolowo University (Nigeria) with seven publications and Koforidua Technical University (Ghana) with six publications.

Figure 7. Most relevant affiliations.

4.1.6. Most Locally and Globally Cited Papers

Previously, the most locally mentioned authors were presented. In this part, the most locally and globally cited papers are presented. The most locally cited documents refer to documents that have been the most cited by the documents included in the same collection. Figure 8 reveals additional information to the information presented in Figure 4 and Figure 6 by showing the most locally cited documents. It gives information about the authors, sources and publication years of these documents. It is observed that the most locally cited documents are those published during the last decade. In contrast to these results, the most globally cited documents presented in Figure 9 include the first one published in 1998 with 250 citations and another published in 1996 with 50 citations. It is followed by the documents recently published that use modern methods of remote sensing.

Figure 8. Most locally cited documents.

Figure 9. Most globally cited papers.

4.1.7. Countries’ Production

The figures previously presented give information about authors, citations and sources. There is no information about the geographical distribution of productions. In contrast, Figure 10 presents the top 10 countries’ production. Note that the document accounting process can assign a given article to several countries if it was obtained from collaboration between two or more countries. Hence, the total number of articles is higher than 53 and sums up to 226. The USA, at the first position, has 40 papers published from 1996 to 2023, which represents 17.7% of production. The top 10 include 5 West African countries: Nigeria (15.49%), Ghana (7.96%), Benin (7.08%), Senegal (6.19%) and Gambia (2.65%). Other countries most probably collaborated with West African countries, which are often open to collaboration with West African countries. This will be formally studied in the country collaboration map of Section 4.1.10.

Figure 10. Country scientific production.

4.1.8. Countries’ Production over Time

Looking at the countries’ production over time, shown in Figure 11, an increasing trend can be observed since 2014. This tendency is likely to continue in the coming years.

Figure 11. Country production over time.

4.1.9. Affiliations’ Production over Time

Figure 12 shows the top five affiliations leading this field. Within West Africa, the University of Abomey-Calavi happens to be the first, whose production began in 2016. It constitutes the university with the highest number of papers (nine).

Figure 12. Affiliations’ production over time.

4.1.10. Country Collaboration Map

It is interesting to see how different countries have collaborated with West African countries on this topic. Figure 13 presents the country collaboration map. The darker blue colour indicates that these countries have more collaborations than others, which are coloured in a lighter blue colour. The lines between countries give information about countries that have collaborated. As it can be seen in this figure, most of the collaborations were made with the United States of America. Other countries include France, the United Kingdom, Luxembourg and Belgium. However, a few collaborations exist between West African countries themselves. In West Africa, Ghana and Nigeria are the countries that have the most collaborations. Ghana is also the country that has mainly collaborated with the United States. Nigeria has mainly worked with the United Kingdom. Senegal has worked with France and the United Kingdom.

Figure 13. Country collaboration map.

4.2. Qualitative Analysis: Relevant Works Review

Previously, a quantitative analysis was undertaken on papers selected based on PRISMA method. In this part, a qualitative analysis is performed on 20 relevant papers, which we have identified from the 53 selected papers. These papers were selected on the basis of an in-depth analysis. This analysis was performed by first excluding the two papers published before 2007 (see Figure 2) as they are less relevant for the current period. The 51 remaining papers were analysed in detail. Papers that present applications of satellite sensors in agriculture and environment are excluded as our study favours practical future directions, leveraging the use of low-cost sensors. Table 3 shows the summary of each paper and gives information about their application fields.

Table 3. Relevant works’ descriptions.

Ref.	Authors (Year)	Title	Domain	Application	Objectives	Outcomes
[22]	J. Quansah et al. (2007)	Remote Sensing Applications for Sustainable Aquaculture in Africa	Agriculture	Fish farming	Review on remote sensing applications for aquaculture in Africa for tilapia and for catfish.	Evaluation of a multi-sensor remote sensing deployment to support sustainable fish farming in Ghana and Kenya.
[27]	C. Hauck et al. (2011)	Soil moisture variability and its influence on convective precipitation over complex terrain	Agriculture	Soil moisture	Analyse the discrepancies between two types of soil moisture from observation and modelling to highlight their impacts on convective precipitation forecasts.	This work shows that convection parameters are linked to the soil moisture.
[28]	Mbonu E. S. et al. (2013)	E-MART: A Novel Smart Home Governance System for a Densely Populated Power Challenged Environment	Environment	Unwanted gaseous substances such as Carbon Monoxide	Solution for dealing with recurring CO poison incidents.	The proposed solution can solve the problem of poison intoxication from carbon monoxide resulting from fuel generators.
[29]	Abdoulaye Kama et al. (2017)	Monitoring the Performance of Solar Street Lights in Sahelian Environment: Case Study of Senegal	Environment	Solar street light	Evaluation of the influence of dust deposits on the degradation of solar street light modules in Senegal.	Better planning of street light solar maintenance.
[30]	O. Elijah et al. (2017)	Enabling Smart Agriculture in Nigeria: Application of IoT and Data Analytics	Agriculture	Smart farming	Use of IoT and data analytics to solve food availability issues.	Proposal of a data center for agricultural data collection such a rainfall, water availability, soil fertility, etc. Proposition of Youth Empowerment Schemes to reinforce youth skills in data analytics and embedded system programming.
[31]	Téeg-Wendé Z. et al. (2018)	Low cost IoT solutions for agriculture fish farmers in Africa: a case study from Burkina Faso	Agriculture and environment	Fish farming, soil moisture and meteorological parameters	Prototype and deploy a low-cost and low-energy IoT system that monitors water quality (for fishery), soil parameters (for agriculture) and environment parameters to help farmers and fish farmers increase the yield of fishing and agriculture.	A system composed of low-cost sensors (powered with batteries and solar panels), long-range communication modules (LoRa and GSM modules) and a specially designed cloud platform (for data analysis and visualization) is developed and deployed.
[32]	Charlotte D. et al. (2018)	Low-Cost IoT Solutions for Fish Farmers in Africa	Agriculture and environment	Fish farming	The solution proposed will allow us to improve fish farming business in Africa by using a water quality monitoring system.	The collected measurements help to point out some recurrent issues and to give recommendations for better water quality and increased fish production yield.
[33]	M. R. Seye et al. (2019)	Work in Progress: A low cost geographical localization system for a more secure coastal artisanal fishery in Senegal	Agriculture	Fish farming	This study proposes a communication system for fishermen to be informed about dugout canoes’ damages and allows for information sharing between fishermen.	The communication system is enabled by long-range technology. Several performance evaluations were performed and a received signal strength of $-95\mathrm{d}\mathrm{B}$m over a 22 km distance was measured.
[34]	Seye, M. R. et al. (2019)	COWShED: Communication within white spots for breeders	Agriculture	Livestock	Ensure communication between herders, in a noncovered zone, based on LoRa transmission. Provide a cheaper solution than satellite communications, which are very expensive for rural populations.	The proposition of a low-cost communication system based on LoRa transmission, which can offer a range of services such as short messages, voice messages, water point status and geographical position. The proposed meshed architecture offers good coverage.
[35]	Omokungbe, O. R. et al. (2020)	Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors	Environment	Air quality	To gather baseline air quality data and assess the impact of prevailing meteorological conditions on particulate matter concentrations in selected residential communities downwind of an iron smelting facility.	This work finds that a smelter’s anthropogenic activities contribute significantly to the high concentration of particulate mass measured at the sites studied.
[36]	Abulude, F. O. et al. (2021)	Preliminary Assessment of Air Pollution Quality Levels of Lagos, Nigeria	Environment	Air quality	Consider the use of air quality index (AQI) satellite data and low-cost real-time, citizen-based PM sensor networks deployed in more than 180 countries and regions, to track Lagos’s air quality.	The air quality index was found to be in all locations “Unhealthy for Vulnerable Groups”.
[37]	McFarlane, C. et al. (2021)	Application of Gaussian Mixture Regression for the Correction of Low Cost $P{M}_{2.5}$ Monitoring Data in Accra, Ghana	Environment	Air quality	Co-locate and calibrate low-cost sensors with $P{M}_{2.5}$ reference monitors to improve data quality.	Use of Gaussian mixture regression to calibrate air quality data and demonstration of improvement over traditional methods.
[38]	Kosisochukwu P. N. et al. (2022)	The Computer Farmer Concept: Human-cyberphysical Systems for Monitoring and Improving Agricultural Productivity in Nigeria	Agriculture	Automated seed planting, piggery, harvesting, vine spraying and fishery	In this paper, digital agriculture concepts such as telerobotic and autonomous farming systems and agricultural ubiquitous energy technologies are studied.	These concepts can be used for mechanized farming.
[39]	K. P. Nnoli et al. (2022)	Longitudinal Ambient $P{M}_{2.5}$ Measurement at Fifteen Locations in Eight Sub-Saharan African Countries Using Low-Cost Sensors	Environment	Air pollution	The goal of this work is to investigate the use of low-cost air quality sensors for $P{M}_{2.5}$ concentrations.	The results showed a high level of particulate matter concentrations in comparison to the recommended values (5 µg/m3). The largest level was observed in Nigeria.
[40]	Sidibe, A. et al. (2022)	Personal Exposure to Fine Particles ($P{M}_{2.5}$) in Northwest Africa: Case of the Urban City of Bamako in Mali	Environment	Air pollution	This work investigated the analysis of personal exposure to PM from indoor and outdoor anthropogenic activities especially for office workers, students, cooks and drivers. The palm-sized optical $P{M}_{2.5}$ sensor is used to evaluate this $P{M}_{2.5}$.	The PM concentrations are above the recommended values, indicating air quality improvement need.
[41]	Pelagie, H. et al. (2022)	Smart Monitoring System using Internet of Things: Application for Agricultural Management in Benin	Agriculture	Livestock (intrusion detection system)	This work provided a surveillance system conceived for monitoring agricultural spaces, which allows us to receive alerts when intrusions occur. It comes to solve problems related to the transhumance process by detecting a cow or oxen 2 m from the system.	A system is developed to detect cows trespassing on farmers’ fields, and a mobile application is used to send out warning messages in anticipation of conflicts.
[42]	Chukwu, T.M. et al. (2022)	Spatial Analysis of Air Quality Assessment in Two Cities in Nigeria: A Comparison of Perceptions with Instrument-Based Methods	Environment	Air quality	This work aims to compare the measurement of air quality obtained from ground-based sensors with the actual perception of people about the air quality. Concentrations of pollutants such as PM, SO2, NO2 and CO were considered and compared with odour, taste and dust indicators of population.	The results show that air quality perceptions seem to be correlated with instrument-based measurements. Therefore, perceptual air quality data can also be used as assessment.
[21]	Degila, J. et al. (2023)	A Survey on Digital Agriculture in Five West African Countries	Agriculture	Review of technologies used in digital agriculture	This work provides the state-of-the-art of smart agriculture in some countries of Western Africa by highlighting the main technologies used: IoT, wireless sensor networks (WSNs), Artificial Intelligence (AI), Big Data, Blockchain, etc.	In Nigeria, the main applications of IoT are precision agriculture, livestock farming, field monitoring for pest control, automatic irrigation, system water quality distribution and monitoring and aquaponics.
[43]	Raheja, G. et al. (2023)	A Network of Field-Calibrated Low-Cost Sensor Measurements of $P{M}_{2.5}$ in Lome, Togo, Over One to Two Years	Environment	Air quality monitoring	Investigation of the air pollution impact in the city of Lome (Togo) by fine particulate matter ($P{M}_{2.5}$) concentrations monitoring over two years, using cheap PurpleAir sensors.	The results showed that, on all measurement sites, more than $87\%$ of collected data exceeded the 2021 WHO $P{M}_{2.5}$ values.
[44]	Hodoli, C.G. et al. (2023)	Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana	Environment	Air quality monitoring	In this study, data obtained from low-cost sensors is added to meteorological data to help us better understand local particulate matter (PM) pollution. The authors demonstrated that low-cost sensors can be used for source identification in urban areas.	This study shows that air quality monitoring can be undertaken by using low-cost sensors to complete the limited air quality data available.

5. Discussion

The quantitative study reveals an average age of 6 years of the papers, indicating that the subject is recently studied in West African countries. There is an increase in published articles from 2014 and an effective growth from 2018, with a peak in 2020. The University of Abomey-Calavi (Benin) is the institution with the highest production, followed by Obafemi Awolowo University (Nigeria) and Koforidua Technical University (Ghana). The countries in West Africa with the top production are, in descending order, Nigeria, Ghana, Benin, Senegal and Gambia. Overall, many international collaborations have been observed, mostly with the USA, followed by France, the UK, Luxembourg and Belgium. However, a key limitation lies in a lack of collaborations between West African countries themselves.

From the qualitative study, we can note that 50% of the 20 case studies are related to environment, while $40\%$ are related to agriculture and $10\%$ to both environment and agriculture. Indeed, several articles present case studies in both fields simultaneously.

The quantitative and qualitative studies give very interesting insights on the current state of the art concerning the adoption of IoT by West African researchers, to solve agricultural and environmental issues. The wide range of use cases clearly demonstrates the interest in this technology and its usefulness, even in the least developed countries. In the following, we discuss insights obtained from investigating the 20 selected studies, both for applications in agriculture and environment. More specifically, we detail the application domains, the sensor data collection techniques, the challenges and finally the opportunities and future directions.

5.1. Application Domains

On the one hand, the applications in agriculture include the following domains:

• Monitoring of livestock and fish farming: this involves the monitoring of the environmental parameters (basins, water quality, etc.) of the animals as well as the internal (health) parameters of the animals themselves.
• Monitoring of crops and soil for smart precision agriculture (moisture, humidity, temperature, pest control, etc.).
• Monitoring of meteorological parameters for agriculture.
• Smart irrigation systems.
• Detection of animal intrusion in fields.

On the other hand, the works with applications in environment include the following domains:

• Monitoring of water quality.
• Monitoring of air quality (unwanted gaseous substance emission and particulate matter).
• Monitoring of meteorological parameters such as rainfall, wind speed, sunshine and air humidity.
• Monitoring of solar street lamps.

5.2. Sensor Data Collection

Various IoT communication technologies like LoRaWAN, ZigBee, Wi-Fi and Bluetooth are used in the reviewed papers. The main conclusions are as follows: the short-range and large-data-rate communication technologies (ZigBee, Bluetooth, Wi-Fi), which were exploited in IoT communication, are now substituted by Low-Power Wireless Area Networks (LPWANs) (LTE-M, NB-IoT, SigFox, LoRaWAN). Indeed, LPWAN protocols allow for long-range communications with low data rates and low power consumption. It allows us to transmit signals using licensed or unlicensed spectra and satisfies IoT embedded system requirements. In particular, the LoRaWAN protocol was developed to minimize the network deployment complexity while increasing network coverage in orders of kilometres and represents the most exploited wireless protocol. Most of the reviewed IoT systems consist of a three-tier architecture: sensor/actuator–gateway–cloud. Data collected by the sensors is forwarded to the gateway, which is in charge of transferring it to the cloud through an internet connection. However, some prototypes do not use a gateway, and data is directly sent from the sensors to the cloud.

As a final remark, it is worth noting that some authors did not use any wireless communication network but only relied on the use of sensors with manual data collection.

5.3. Challenges

Despite its current level of development, IoT technology faces many challenges that hinder its widespread adoption in West Africa. It can be noticed from the reviewed papers that the presented use cases are almost all in the steps of prototypes or proof-of-concepts, showing that IoT is far from being mature in this region. Regardless of the low cost of existing hardware and technologies, there is still a need to reduce their costs and make them readily available, without having to order them from other continents. The urgent need to cut costs is reflected in the fact that almost all prototypes use low-cost equipment. Right after that comes the challenge of low-energy devices. West African countries are not yet electrically self-sufficient, and power cuts are recurrent. In particular, there are large areas (such as desert regions) where no electrical grid is present. Therefore, the West African IoT systems need low-energy and self-powered devices to cope with this challenge. Another important challenge is the lack of communication infrastructures. It was noticed above that LoRa technology is suitable for the West African context. However, it is not possible for each individual to deploy a LoRa network. It would be better to have existing infrastructures that can be mutually used by IoT systems developers. In the same vein, the lack of mature internet infrastructure is a big obstacle, as cloud data storage, processing and analysis rely on strong internet connectivity. The last challenge that must be met is the lack of technological skills. This includes the ability to design, develop, deploy and maintain (replace or repair malfunctioning components) IoT systems.

5.4. Opportunities and Future Directions

In view of all discussions previously made, we summarize a set of promising opportunities and future directions for African countries:

• Monitoring of waterways, the water levels and the quality to avoid or manage floods during the rainy seasons and safeguard the biodiversity in waterways.
• Accurate forecasting of drought and rainfall due to climate change, to enable making appropriate decisions.
• Collection of temperature data, which is useful to predict periods of peak temperatures currently experienced in West Africa, which are almost unbearable without air conditioning.
• Monitoring or tracking of animals in their natural environment or in the livestock sector using IoT. The animals in the herd may be monitored to observe their behavior, state of health and reactions to potential threat.
• Crop and soil type prediction using emerging technologies to identify the compatibility between crops and soils and the season suitable for the crop production. In other words, it means the use of digital technologies to make the variable-rate technology (VRT) more effective in these countries. The variable-rate technology enables growers to vary the rate of crop inputs such as fertilizer, soil amendments, irrigation and agricultural chemicals.
• Greenhouse farming using emerging technology such as IoT, Artificial Intelligence, robotics and data analytics. Agricultural greenhouses offer an ideal solution for improving crop productivity, especially in Africa, where climatic conditions can be extreme. Its main advantages are protection against pests and diseases, optimized use of water resources and year-round production.
• Improvement of the fishing system using precision fishing for small- and large-scale fishing. Precision fishing consists of the use of advanced tools and technologies to optimize fishing operations and management. Fishermen can optimize fish production while fishing in a more sustainable way.
• Development of rice-fish farming systems to jointly improve the rice and fish production. It is based on a mutually beneficial relationship between rice and fish in the same agroecosystem, most commonly with freshwater fish.

These directions are directly useful for research and development companies, educators, researchers and NGOs in West Africa.

6. Conclusions

This bipartite review consists of a bibliometric analysis and a systematic review on the application of IoT in agriculture and environment in West Africa. Agriculture serves as the primary livelihood for numerous individuals, encompassing farmers, agronomists, transporters and more. It holds the potential to augment incomes, enhance food security and positively impact the environment. Nonetheless, our food systems are accountable for various environmental challenges. Although the adoption of IoT in agriculture and environmental management has proven highly efficient on a global scale, its implementation remains underdeveloped in Africa, particularly in West Africa.

On the one hand, the bibliometric analysis presents the current landscape of the application of IoT in both agriculture and environment in West Africa. The bibliometric study encompasses 53 studies, obtained from the Web of Science database, of on average 6 years of age, indicating that the topic is newly studied in West African countries. Precisely, there is an increase in published papers from 2014 and an effective growth from 2018, with a peak in 2020. The University of Abomey-Calavi (Benin) has the highest paper production, followed by Obafemi Awolowo University (Nigeria) and Koforidua Technical University (Ghana). The leading countries in West Africa are Nigeria, Ghana, Benin, Senegal and Gambia with, respectively, 15.49%, 7.96%, 7,08%, 6.19% and 2.65% paper production. Overall, many collaborations have been observed, mostly with the USA, followed by France, the UK, Luxembourg and Belgium. However, a key limitation lies in a lack of collaborations between West African countries themselves.

On the other hand, the qualitative analysis reviews twenty relevant papers. The findings from the literature review indicate that 50% of the documents relate to the environment, 40% are associated with agriculture and 10% to both environment and agriculture. A total of 80% of these papers have been published in the past five years (2018–2023). The latter cover different applications, mainly in air quality or pollution monitoring. There are also applications, about environment, in water level monitoring for flood prevention, monitoring of unwanted gaseous substance emission (Carbon Monoxide) and meteorological parameter (wind speed, air humidity, rainfall, sunshine) monitoring. Regarding IoT applications in agriculture, the works include fishery or fish farming, livestock, farming, automated seed planting, piggery, harvesting and vine spraying soil moisture. They are mainly focused on aquatic ecosystem monitoring, water monitoring (pH, water temperature and dissolved oxygen), field monitoring (soil moisture, pest control) and automatic irrigation systems. Observing instances where networks are absent, relying solely on sensors and manual data collection, the future of IoT deployment for data collection in West Africa holds immense promise. This technological advancement should help to address pertinent challenges in agriculture and the environment within the region.

The proposed future directions are directly useful for research and development companies, educators, researchers and NGOs in West Africa. The highlighted directions include monitoring wildlife and livestock using IoT to observe behavior, health and threat responses, aiding biodiversity conservation and mitigating human–animal conflicts through geolocation tracking. Greenhouse farming, leveraging IoT, AI, robotics and data analytics, can also enhance crop productivity with pest protection, efficient water use and continuous production. Precision fishing, employing advanced tools, optimizes both small- and large-scale operations sustainably.

As a final note, we would like to point out the limitations of this review and related perspectives. A main shortcoming is that this review considers the extraction of articles from a single database, Web of Science. While this is a trustable source, the inclusion of other databases such as, e.g., Scopus, could enlarge the number of reviewed articles and provide additional findings and research trends. Other interesting perspectives include adding other relevant aspects in the qualitative study such as interoperability and security.