Assessment of Deep Learning for Crop Type Mapping and Yield Prediction in Sub-Saharan Africa: A Systematic Review for Operational Agricultural Monitoring in Nigeria

References

[1] Agboka, K. M., Tonnang, H. E. Z., Abdel-Rahman, E. M., Odindi, J., Mutanga, O., & Niassy, S. (2022). Data-driven artificial intelligence (AI) algorithms for modelling potential maize yield under maize–legume farming systems in East Africa. Agronomy, 12(12), 3085.

[2] Alabi, T. R., Abebe, A. T., Chigeza, G., & Fowobaje, K. R. (2022). Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa. Remote Sensing Applications: Society and Environment, 27, 100782.

[3] Bohra, A., Nottmeyer, S., Ren, C., Chen, S., & Ma, Y. (2025). Advancing corn yield mapping in Kenya through transfer learning. Remote Sensing, 17(10), 1717.

[4] Cedric, L. S., Adoni, W. Y. H., Aworka, R., Zoueu, J. T., Mutombo, F. K., Krichen, M., & Kimpolo, C. L. M. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agricultural Technology, 2, 100049.

[5] Chew, R., Rineer, J., Beach, R., O’Neil, M., Ujeneza, N., Lapidus, D., Miano, T., Hegarty-Craver, M., Polly, J., & Temple, D. S. (2020). Deep neural networks and transfer learning for food crop identification in UAV images. Drones, 4(1), 7.

[6] de Villiers, C., Mashaba-Munghemezulu, Z., Munghemezulu, C., Chirima, G. J., & Tesfamichael, S. G. (2024). Assessing maize yield spatiotemporal variability using unmanned aerial vehicles and machine learning. Geomatics, 4(3), 213–236.

[7] Duke, O. P., Alabi, T., Neeti, N., & Adewopo, J. (2022). Comparison of UAV and SAR performance for crop type classification using machine learning algorithms: A case study of humid forest ecology experimental research site of West Africa. International Journal of Remote Sensing, 43(11), 4259–4286.

[8] Fofanah, I. D. (2026). Can machine learning forecast rice yields in data-constrained settings? Satellite climate data, national crop statistics, and lessons from Sierra Leone. arXiv preprint arXiv:2606.13959.

[9] Jemo, M., Devkota, K. P., Epule, T. E., Chfadi, T., Moutiq, R., Hafidi, M., ... & Jibrin, J. M. (2023). Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria. Frontiers in Plant Science, 14, 1120826.

[10] Kaneko, A., Kennedy, T., Mei, L., Sintek, C., Burke, M., Ermon, S., & Lobell, D. (2019, June). Deep learning for crop yield prediction in Africa. In ICML Workshop on Artificial Intelligence for Social Good (pp. 33–37).

[11] Karst, I. G., Mank, I., Traoré, I., Sorgho, R., Stückemann, K.-J., Simboro, S., Sié, A., Franke, J., & Sauerborn, R. (2020). Estimating yields of household fields in rural subsistence farming systems to study food security in Burkina Faso. Remote Sensing, 12(11), 1717.

[12] Kerner, H., Nakalembe, C., & Becker-Reshef, I. (2020). Field-level crop type classification with k nearest neighbors: A baseline for a new Kenya smallholder dataset. arXiv preprint arXiv:2004.03023.

[13] Kuradusenge, M., Hitimana, E., Mtonga, K., Gatera, A., Habiyaremye, J., Ngabonziza, J., ... & Mukasine, A. (2024). SMART-CYPS: An intelligent internet of things and machine learning powered crop yield prediction system for food security. Discover Internet of Things, 4(1), 23.

[14] Lambert, M. J., Traoré, P. C. S., Blaes, X., Baret, P., & Defourny, P. (2018). Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt. Remote Sensing of Environment, 216, 647–657.

[15] Lee, D., Davenport, F., Shukla, S., Husak, G., Funk, C., Harrison, L., ... & Verdin, J. (2022). Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning. Global Food Security, 33, 100643.

[16] Lee, S., Jeong, Y., Son, S., & Lee, B. (2019). A self-predictable crop yield platform (SCYP) based on crop diseases using deep learning. Sustainability, 11(13), 3637.

[17] Ly, R., Dia, K., & Diallo, M. (2021). Remote sensing and machine learning for food crop production data in Africa post-COVID-19. arXiv preprint arXiv:2108.10054.

[18] Mupangwa, W., Chipindu, L., Nyagumbo, I., Mkuhlani, S., & Sisito, G. (2020). Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa. SN Applied Sciences, 2(5), 952.

[19] Obahoundje, S., Tilahun, S. A., Zemadim, B., & Schmitter, P. (2025). A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield. Environmental Research Communications, 7(10), 102001.

[20] Olatinwo, D. D., Myburgh, H. C., De Freitas, A., & Abu-Mahfouz, A. (2026). Explainable data-driven approach for smart crop yield prediction in Sub-Saharan Africa: Performance and interpretability analysis. Agriculture, 16(8), 826.

[21] Olayinka, T. C., Adetunmbi, A. O., Obe, O. O., Ibam, E. O., & Olayinka, A. S. (2025). A data-driven machine learning approach toward an improved maize crop production. Franklin Open, 100334.

[22] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.

[23] Rustowicz, R. M., Cheong, R., Wang, L., Ermon, S., Burke, M., & Lobell, D. (2019). Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 75–82).

[24] Samasse, K., Hanan, N. P., Anchang, J. Y., & Diallo, Y. (2020). A high-resolution cropland map for the West African Sahel based on high-density training data, Google Earth Engine, and locally optimized machine learning. Remote Sensing, 12(9), 1436.

[25] Sisheber, B., Marshall, M., Mengistu, D., & Nelson, A. (2023). Assimilation of earth observation data for crop yield estimation in smallholder agricultural systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 557–572.

[26] Sitienei, M. C. (2024). Machine learning in agriculture with application in maize (Zea mays) yield prediction modeling in Uasin Gishu County, Kenya [Doctoral dissertation, University of Eldoret].

@article{1719126,
      author = {Dr. Adigun Abbas Bolaji, Danfulani Abdulrahman Usman, Dauda Samuel Impa; Maisamari Jesse Danladi, Thomas Tisere Bindas; Jonathan Joshua Benthang, Shadrach Iliya; Arowosegbe Olakunle Justice},
      title = {Assessment of Deep Learning for Crop Type Mapping and Yield Prediction in Sub-Saharan Africa: A Systematic Review for Operational Agricultural Monitoring in Nigeria},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2533-2551},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719126.pdf},
      abstract = {The application of deep learning techniques in agriculture has gained significant attention due to their potential to improve crop monitoring, classification, and yield forecasting. However, evidence regarding their adoption, performance, and operational suitability within Sub-Saharan Africa remains fragmented. This study conducted a systematic literature review to synthesize existing evidence on deep learning architectures for crop type mapping and yield prediction in Sub-Saharan Africa. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework. Literature was retrieved from Scopus, Google Scholar, Frontiers, and MDPI databases using predefined search strings. An initial 4,777 records were identified, from which 27 studies met the inclusion criteria and were subjected to qualitative synthesis. The review examined deep learning architectures employed, remote sensing and geospatial datasets utilized, model performance, and emerging trends in agricultural monitoring. The findings revealed that Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Long Short-Term 
Memory (LSTM) networks, Deep Neural Networks (DNNs), transfer learning frameworks, and hybrid models were the dominant architectures used for crop type mapping and yield prediction. Sentinel-2, Landsat, MODIS, UAV imagery, climatic datasets, and soil information constituted the primary data sources. The review further showed that deep learning models achieved high classification accuracies and strong predictive performance, particularly when multiple datasets were integrated through data fusion techniques. Emerging trends identified include transfer learning, explainable artificial intelligence, hybrid deep learning architectures, attention-based models, UAV-assisted monitoring, and IoT-enabled agricultural systems. The study concludes that deep learning technologies offer substantial potential for improving agricultural monitoring and yield forecasting across Sub-Saharan Africa. However, challenges relating to data scarcity, model transferability, and unequal geographical coverage remain. The study recommends increased investment in open agricultural datasets, multi-source data integration, and explainable artificial intelligence frameworks to enhance the scalability and operational deployment of deep learning systems in the region.},
      keywords = {Deep Learning, Crop Type Mapping, Yield Prediction, Remote Sensing, Agricultural Monitoring, Sub-Saharan Africa, Artificial Intelligence.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719126}
  }