By 2022, 42.39% of the sub‐Saharan Africa (SSA) population was living in urban areas. This urbanization correlates with increasing poverty, unemployment, food insecurity, environmental pollution, and the prevalence of informal settlements. These challenges worsened urban food insecurity during the COVID‐19 pandemic in SSA cities. This review analyzed the role of urban farming system as a pivotal means to enhance urban food security, incorporating socioeconomic integration and environmental sustainability. The analysis is grounded in a systematic review using specific keywords, evaluating 46 articles and institutional reports related to the subject. The results revealed that 3.62% of SSA countries have implemented national urban governance and policies with minimal focus on urban farming. Rapid urbanization, urban population growth, and climate change are key factors contributing to cities' vulnerabilities to food insecurity in SSA. Predominantly characterized by horticultural practice, urban farming enhances the food supply system, nutritious security, jobs and income generation, reduces transportation costs, promotes the consumption of fresh food, and mitigates food loss in cities. Despite its importance, urban farming in SSA encounters several challenges: (i) urbanization governance and policy, (ii) knowledge and technology in urban farming, (iii) access to land and water, (iv) financing and capacity building for urban farming, and (v) environmental pollution. SSA countries need a coordinated mix of urbanization policies and technological advancements to integrate innovative urban farming methods, bolstering cities’ resilience to food insecurity. Implementing these measures could advance the achievement of sustainable development goals 2 and 11 in SSA cities.

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine’s AI system analyzes these images to provide accurate diagnoses. The application’s offline functionality ensures accessibility even in remote areas with limited Internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture

Ouagadougou and Bobo-Dioulasso remain the two major urban centers in Burkina Faso with an increasing trend in human footprint. The research aimed at analyzing the Land Use/Land Cover (LULC) dynamics in the two cities between 2003 and 2021 using intensity analysis, which decomposes LULC changes into interval, category and transition levels. The satellite data used for this research were composed of surface reflectance imagery from Landsat 5, Landsat 7 and Landsat 8 acquired from the Google Earth Engine Data Catalogue. The Random Forest, Support Vector Machine and Gradient Tree Boost algorithms were employed to run supervised image classifications for four selected years including 2003, 2009, 2015 and 2021. The results showed that the landscape is changing in both cities due to rapid urbanization. Ouagadougou experienced more rapid changes than Bobo-Dioulasso, with a maximum annual change intensity of 3.61% recorded between 2015 and 2021 against 2.22% in Bobo-Dioulasso for the period 2009–2015. The transition of change was mainly towards built-up areas, which gain targeted bare and agricultural lands in both cities. This situation has led to a 78.12% increase of built-up surfaces in Ouagadougou, while 42.24% of agricultural land area was lost. However, in Bobo-Dioulasso, the built class has increased far more by 140.67%, and the agricultural land areas experienced a gain of 1.38% compared with the 2003 baseline. The study demonstrates that the human footprint is increasing in both cities making the inhabitants vulnerable to environmental threats such as flooding and the effect of an Urban Heat Island, which is information that could serve as guide for sustainable urban land use planning.

High-frequency meteorological observations from automatic weather stations (AWS) are frequently affected by substantial data gaps in data-sparse regions such as West Africa, limiting their usability for climate analysis and decision-making. This study presents a machine learning-based framework for reconstructing missing hourly observations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products. The framework was applied to a network of over 50 AWS across 10 West African countries over the period 2017–2025, targeting seven meteorological variables: air temperature, relative humidity, global solar radiation, atmospheric pressure, precipitation, wind speed, and wind direction. Gradient boosting models (XGBoost, LightGBM, and CatBoost) were trained following a station-wise and variable-wise strategy, yielding over 300 variable-specific models. Detailed quantitative results are reported for four representative stations spanning distinct agro-climatic zones (Sahelian, Sudanian, coastal, and humid tropical). Air temperature and atmospheric pressure exhibit the highest reconstruction skill, with R² values typically exceeding 0.90, while relative humidity and global solar radiation achieve R² between 0.80 and 0.92. Precipitation and wind speed showed lower reconstruction skill than thermodynamic variables, reflecting their intermittency and sensitivity to local-scale processes. Wind direction, evaluated separately using circular statistics after recombination into degrees, exhibited the largest angular errors, highlighting the difficulty of reconstructing directional variability from large-scale predictors alone.