A Survey on Digital Agriculture in Five West African Countries
Abstract
:1. Introduction
2. Materials and Methods
2.1. Bibliometric Survey on Digital Agriculture
2.2. Survey on Technologies Used in Digital Agriculture
2.3. Methodology
3. Results and Discussion
3.1. Bibliometric Study
3.1.1. Annual Scientific Production
3.1.2. Relevant Source
3.1.3. Author Countries
3.1.4. Author Organizations
3.1.5. Co-Occurrence Network
3.2. Digital Technologies Survey in the Five West African Countries
3.3. Lessons Learned
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Papers | Digital Technology | Details | Applications |
---|---|---|---|
Benin | |||
J. Aoga et al. [26] | AI, machine learning | random forest (RF) and extreme gradient boosting (XGB) | Crops, production (forecasting of soil properties) |
Burkina Faso | |||
Y. E. Gouly and A. Gusov [27] | digital platforms, artificial intelligence, and robotics | Agro-industrial platform | Crops yield production (cereal and rice production), livestock and fisheries production |
E. Pignede et al. [28] | AI, machine learning, automatic learning | rainfall data, temperature data, and sugarcane yield analysis | Sugarcane yield forecasting using the random forest method |
Gloria C. Okafor et al. [29] | Satellite images | Rain-fed agriculture prediction | cassava, yam, groundnut, maize, and sorghum crops production |
G. Forkuor et al. [30] | IoT, WSN, and IA | Satellite spectral data, terrain and climatic variables analyzed based on multiple linear regression (MLR), random forest regression (RFR), support vector machines for regression (SVM), and stochastic gradient boosting (SGB) | prediction of soil properties |
T. W. Zoug- more et al. [31] | IoT | Sensor measure parameters, such as pH, dissolved oxygen, water temperature, soil moisture, and meteorological parameters (wind speed, air humidity, rainfall, sunshine) | soil moisture properties for papaya and banana crop production |
Cote d’Ivoire | |||
M-P. Soro et al. [32] | AI, machine learning | Artificial neural network | Riverine water monitoring |
E. Pignede et al. [28] | AI, machine learning, automatic learning | Rainfall data, temperature data, and sugarcane yield analysis | Sugarcane yield forecasting using the random forest method |
Ghana | |||
S. Musah et al. [33] | Blockchain | Transparency and traceability enhancement, unethical activities mitigation | Cocoa bean food supply chains |
S. Vyas et al. [34] | AI and blockchain | Food supply chains and drug supply chains management, quality maintenance, and intelligent prediction | Drug supply chain |
D. Wally et al. [35] | Big data and ICT | Satellites and remote sensors, mobile phone and remote sensors, accounting software, and GPS | farmers income increasing, data quality, ownership, and accessibility |
N. K. A. Appiah-Badu et al. [36] | AI, machine learning | Random forest and extreme gradient boosting method for rainfall prediction, temperature (minimum and maximum), relative humidity, sunshine hours, and wind speed data prediction | ecological zone |
K. A. Nketia [37] | AI, machine learning | Random forest, extreme gradient boosting algorithms | soil water storage in landscape |
L. S. Cedric et al. [38] | AI and big data | Crops yield prediction weather data and chemical data | predict bananas, dry beans, cassava, rice, maize, and seed cotton |
C. Nyamekye et al. [39] | AI, machine learning | Evaluation of the transitions among the major land use/land cover categories in machine learning algorithms (random forest) and intensity analysis | Environment |
Nigeria | |||
U. S. Abdul- lahi et al. [40] | IoT-LoRaWAN | Precision agriculture that uses analytic measurements to optimize farming decisions | Livestock farming: IoT helps farmers in making lists, preparing reports, sorting cows by category, and tracking each animal’s overall lifetime |
U. C. Njoku et al. [41] | Wireless sensor networks (WSNs)- LoRaWAN | Remote monitoring system of the environmental weather and soil conditions of the farmland to trigger irrigation automatically | field monitoring for rural farmers and automatic irrigation system |
L. A. Ajao et al. [2] | IoT: WSN-Wi-Fi | Agro-climatic field parameters sensing using soil pH meter, soil moisture, and environmental temperature and humidity sensors. Energy consumption system managing using algorithmic state machine technique | Regular farm crops monitoring using the low energy consumption system |
H. Borg- wardt [42] | Digital platforms, GPS tracking solution with LoRaWAN | Survey on smart farming and adoption | digital applications for market access and crowd farming, digital applications adoption |
O. Elijah et al. [43] | IoT and data analysis | The application of IoT technologies and data analysis in agriculture: Sensing monitoring, use of RFID, etc. | Plant farms, animal farms, automated machinery, aquaponics |
A. M. Manoha-ran, and V. Rathinasabapthy [44] | IoT-LoRaWAN | The LoRa mote along with sensors are placed in water tanks at villages and within corporation limits | smart village: Water quality monitoring and distribution, chemical leakage detection in rivers |
N. Bore et al. [45] | blockchain | Agribusiness digital wallet (ADW) system development, which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem | Small-scale farming formalization digital trust establishment among the agriculture stakeholders |
E. Omo Ojugo [46] | Big data | Big data analytics adoption for farming practices enhancement | Yield improvement |
M. A. Umar et al. [47] | AI, machine learning, and deep learning | Models, such as ANN, SVM, EL/RF, ANN-XY, CNN, MLR, hybrid ANN, LSTM, LR/Bagging tree, FFNN, DT, BP, GWR, and XGBoost are used | Crop management, livestock management, water, and soil management |
R. W. Bello et al. [48] | AI, machine learning and deep learning | Enhanced mask region-based convolutional neural networks (mask RCNN) | breeding improvement |
Nwabueze, C. A. et al. [49] | WSN and GSM communication technology | Design of smart irrigation system to improve agricultural yield | Monitoring and control of various environmental factors, such as soil moisture and temperature |
Ajibola O. [50] | WSN applications | Review on WSN: Measure of parameters, such as pH level, electrical conductivity, oxidation-reduction Potential (ORP), turbidity | Monitoring of the water quality distributed in the country |
Asogwa, T. C. et al. [51] | Sensor nodes deployed in farm field to measure air temperature, relative humidity, and soil moisture. They are also used to keep livestock healthier with a minimum use of resources | Use of sensor nodes to monitor micro-climates in a potato, millet, or cassava field. Determination of the pH level and temperature inside the cow‘s rumen | |
Bolaji A. A. et al. [52] | Electronic-cattle health monitoring using WSN | Cattle parameters monitoring, such as farming environment, cattle movement, cattle health, reproduction management, lactation and rumination monitoring | |
Abdulkadir S. B. et al. [53] | Design of forest fire monitoring system using sensors. Arduino microcontroller is used as the brain of the research to regulate input from the AMG8833 sensor and GPS Ubox 6 M | hotspots detection from 19.25 to 122.5 °C by sensor nodes, which present the capcacity to detect fire in the distance range of 2.5 to 10 m | |
Trisha D. B. et al. [54] | Remote sensing techniques of draft monitoring, traditional drought, and surface water body monitoring | Techniques tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resource |
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Degila, J.; Tognisse, I.S.; Honfoga, A.-C.; Houetohossou, S.C.A.; Sodedji, F.A.K.; Avakoudjo, H.G.G.; Tahi, S.P.G.; Assogbadjo, A.E. A Survey on Digital Agriculture in Five West African Countries. Agriculture 2023, 13, 1067. https://doi.org/10.3390/agriculture13051067
Degila J, Tognisse IS, Honfoga A-C, Houetohossou SCA, Sodedji FAK, Avakoudjo HGG, Tahi SPG, Assogbadjo AE. A Survey on Digital Agriculture in Five West African Countries. Agriculture. 2023; 13(5):1067. https://doi.org/10.3390/agriculture13051067
Chicago/Turabian StyleDegila, Jules, Ida Sèmévo Tognisse, Anne-Carole Honfoga, Sèton Calmette Ariane Houetohossou, Fréjus Ariel Kpedetin Sodedji, Hospice Gérard Gracias Avakoudjo, Souand Peace Gloria Tahi, and Achille Ephrem Assogbadjo. 2023. "A Survey on Digital Agriculture in Five West African Countries" Agriculture 13, no. 5: 1067. https://doi.org/10.3390/agriculture13051067
APA StyleDegila, J., Tognisse, I. S., Honfoga, A.-C., Houetohossou, S. C. A., Sodedji, F. A. K., Avakoudjo, H. G. G., Tahi, S. P. G., & Assogbadjo, A. E. (2023). A Survey on Digital Agriculture in Five West African Countries. Agriculture, 13(5), 1067. https://doi.org/10.3390/agriculture13051067