Jeff Dacosta Osei

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools.

Unmanned aerial vehicle (UAV) remote sensing has evolved from experimental imaging into an operational diagnostic infrastructure supporting climate-smart agriculture through high-resolution, flexible, and timely crop observation. This review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion to evaluate their combined potential for monitoring heterogeneous smallholder systems. A PRISMA-guided analysis of 59 studies (2013–2024) classified sensing architectures, analytical approaches, and application domains across diverse agroecological contexts. Integrated UAV–AI frameworks improve detection of crop stress, yield variability, biomass distribution, and phenological dynamics compared with conventional monitoring, particularly when multimodal sensor data are fused with satellite and ground observations. Predictive performance and diagnostic reliability increase when spectral, thermal, and structural datasets are analyzed jointly using machine-learning or deep-learning models. However, scalability remains constrained by operational, infra-structural, and regulatory factors, especially in resource-limited systems. These findings demonstrate that integrated sensing–analytics systems form a critical foundation for scalable climate-smart agricultural transformation and data-driven decision support across farm, landscape, and institutional scales.

This study assessed land use and land cover (LULC) changes, ecosystem service values (ESVs), and their interactions in the Atwima Nwabiagya South District of Ghana between 2015 and 2025. Satellite image classification identified five LULC classes: vegetation, bare land, built-up areas, mined sites, and waterbodies. Over the decade, vegetation cover declined sharply from 56.1% to 34.4%, while bare land expanded from 32.6% to 48.3% and mined areas increased from 0.3% to 3.2%. Using a benefit transfer approach, total ecosystem service value decreased from about USD 765.4 million in 2015 to USD 551.2 million in 2025, largely due to the conversion of high-value vegetation and waterbodies into degraded land. The findings link land degradation primarily to rapid urban expansion and intensified mineral extraction. The study highlights the need for sustainable land management, ecosystem restoration, and district-level monitoring systems to support land-use planning, community-led stewardship, and Ghana’s Land Degradation Neutrality objectives.This study assessed land use and land cover (LULC) changes, ecosystem service values (ESVs), and their interactions in the Atwima Nwabiagya South District of Ghana between 2015 and 2025. Satellite image classification identified five LULC classes: vegetation, bare land, built-up areas, mined sites, and waterbodies. Over the decade, vegetation cover declined sharply from 56.1% to 34.4%, while bare land expanded from 32.6% to 48.3% and mined areas increased from 0.3% to 3.2%. Using a benefit transfer approach, total ecosystem service value decreased from about USD 765.4 million in 2015 to USD 551.2 million in 2025, largely due to the conversion of high-value vegetation and waterbodies into degraded land. The findings link land degradation primarily to rapid urban expansion and intensified mineral extraction. The study highlights the need for sustainable land management, ecosystem restoration, and district-level monitoring systems to support land-use planning, community-led stewardship, and Ghana’s Land Degradation Neutrality objectives.

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification.

Cocoa production in West Africa—dominated by Côte d’Ivoire, Ghana, Nigeria, Cameroon, and Togo—faces interconnected agronomic, environmental, and socio-economic challenges that limit productivity and threaten smallholder livelihoods. Integrating Regenerative Agriculture (RA), Unmanned Aerial Systems (UAS), and Artificial Intelligence (AI) present a transformative framework for achieving sustainable and climate-resilient cocoa farming. This review synthesizes evidence from 2000 to 2024 and establishes a tri-axial model that unites ecological regeneration, spatial diagnostics, and predictive intelligence. Regenerative practices such as composting, mulching, cover cropping, and agroforestry rebuild soil organic matter, enhance biodiversity, and strengthen ecosystem services. UAS-based multispectral, thermal, and LiDAR sensing provide high-resolution insights into canopy vigor, nutrient stress, and microclimatic variability across heterogeneous cocoa landscapes. When coupled with AI-driven analytics for crop classification, disease detection, yield forecasting, and decision support, these tools collectively enhance soil organic carbon by 15–25%, stabilize yields by 12–28%, and reduce fertilizer and water inputs by 10–20%. The integrated RA–UAS–AI framework also facilitates carbon-credit quantification, ecosystem-service valuation, and inclusive participation through cooperative drone networks. Overall, this convergence defines a precision-regenerative model tailored to West African cocoa systems, aligning productivity gains with ecological restoration, resilience, and regional sustainability.

Water scarcity and efficient water resource management are growing concerns in the face of climate change and increasing demands for freshwater. This study focuses on assessing ecosystem water-use efficiency by investigating the ratio of Gross Primary Productivity (GPP) to evapotranspiration within Ghana. The study sought to explain the interactions of vegetation productivity and water use, shedding new light on the efficient use of this precious resource. The current research provides an extensive analysis of the Ecosystem Water-Use Efficiency (WUE) in Ghana, using satellite remote sensing images to map water-stressed regions throughout the country. A spatial analysis by us found that a large area of Ghana, spanning 4090750Ha, had low WUE levels in evidence, typical of water-stressed ecosystems. Of notable interest, the Upper West and Savannah regions had the greatest cover of water-stressed vegetation of 605650Ha and 1263150Ha, respectively, in the grasslands, and the Bono region had a dominance of water stress in the Savana land use category. The OTI region had a special case, wherein both Savannas and riverine vegetation were prone to water stress. These observations highlighted the importance of region-specific, target-led interventions to boost water-use efficiency, conserve ecosystems, and adopt water resource management practices in Ghana.