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

• Author(s): Dr. Adigun Abbas Bolaji; Danfulani Abdulrahman Usman; Dauda Samuel Impa; Maisamari Jesse Danladi; Thomas Tisere Bindas; Jonathan Joshua Benthang; Shadrach Iliya; Arowosegbe Olakunle Justice
• Paper ID: 1719126
• Page: 2533-2551
• Published Date: 24-06-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 12 June-2026

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.

Citations

IRE Journals:
Dr. Adigun Abbas Bolaji, Danfulani Abdulrahman Usman, Dauda Samuel Impa; Maisamari Jesse Danladi, Thomas Tisere Bindas; Jonathan Joshua Benthang, Shadrach Iliya; Arowosegbe Olakunle Justice "Assessment of Deep Learning for Crop Type Mapping and Yield Prediction in Sub-Saharan Africa: A Systematic Review for Operational Agricultural Monitoring in Nigeria" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2533-2551

IEEE:
Dr. Adigun Abbas Bolaji, Danfulani Abdulrahman Usman, Dauda Samuel Impa; Maisamari Jesse Danladi, Thomas Tisere Bindas; Jonathan Joshua Benthang, Shadrach Iliya; Arowosegbe Olakunle Justice "Assessment of Deep Learning for Crop Type Mapping and Yield Prediction in Sub-Saharan Africa: A Systematic Review for Operational Agricultural Monitoring in Nigeria" Iconic Research And Engineering Journals, 9(12)