Work

Artificial Intelligence and Rice Yield Prediction in Casamance: A Systematic Review of Methodological Advances and Implementation Challenges

Year: 2026

Type: review

Abstract: In a context of climate change threatening food security in sub-Saharan Africa, this systematic review assesses the potential of artificial intelligence for forecasting rice yields in Casamance, Senegal's agricultural heartland. The analysis of 32 foundational studies reveals the superiority of ensemble algorithms (Random Forest, XGBoost) and deep neural networks (LSTM) over traditional statistical methods, while the integration of ERA5 climate reanalyses mitigates the lack of field data. As the first synthesis establishing a methodological framework specific to Casamance rice farming, this research identifies scientific gaps and proposes implementation pathways for resilient agriculture)

Source: International Conference on Electrical, Computer and Energy Technologies (ICECET)

DOI: 10.1109/icecet65726.2026.11632342

Authors: Serigne Mor Toure, Khalifa Gaye, Djibril Sarr, Ousmane Senghor

Institution: Ziguinchor University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

OpenAlex ID: W7202292595

FWCI:

Cites: 23

Cited by:

Related to: 10