Opportunities and challenges for monitoring maize production in sub-Saharan Africa: A comprehensive bibliometric analysis of remote sensing applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maize is a cornerstone of food systems worldwide, serving as both a staple crop and a primary source of income in many parts of the Global South. Ensuring its sustainable production is vital for food security and poverty alleviation. Remote sensing provides powerful tools for monitoring crop growth, estimating yield, and informing management practices. However, despite its rapid expansion in agriculture, there has been no comprehensive synthesis of how remote sensing has been applied specifically to maize production. This study addresses this knowledge gap through a bibliometric analysis of publications on remote sensing and maize from 1925 to 2024. Publication data was retrieved from the Web of Science and Scopus databases and analysed to assess temporal trends, global research distribution, collaboration networks, and thematic directions. The results show a significant increase in research output, from a single publication in 1925 to 488 in 2024, with accelerated growth after 2001. The literature is heavily skewed towards the Global North, with China emerging as the most prolific contributor, reporting 1012 single-country publications (SCP) and 257 multi-country publications (MCP). In contrast, the Global South remains underrepresented, highlighting structural imbalances in research capacity and funding. The review demonstrates that while remote sensing applications in maize production have expanded rapidly, their benefits are unevenly distributed. The findings suggest that increased investments in research infrastructure, capacity building, and funding in the Global South are crucial to bridging the gap with the Global North. Such efforts would promote more equitable knowledge generation and improve the global response to the challenges of food insecurity and climate change. The synthesis of trends, research gaps, and emerging directions presented here provides a foundation for advancing scientific inquiry and shaping policy frameworks that strengthen maize production through remote sensing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.072 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it