AI-driven approaches in precision agriculture for strawberry production: A systematic literature review

This study presents a systematic literature review (2015-2025) on the integration of artificial intelligence and remote sensing technologies in strawberry ( Fragaria × ananassa ) crop, following the PRISMA 2020 methodology. The review explores applications in yield prediction, quality estimation, harvesting automation, disease, pest, and weed detection, and damage detection. It covers various sensing technologies, including red-green-blue (RGB), hyperspectral, multispectral, thermal, and stereo imaging, as well as digital agriculture platforms such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), robotic systems, laboratory-based setups, and stationary sensors. Results highlight the dominance of RGB imaging because it is affordable and easily accessible, as well as the increasing significance of robotics and hyperspectral analysis in automation. The novelty of this review lies in its systematic review and integration of artificial intelligence methods, sensing technologies, and agricultural platforms used in strawberry production. By integrating the latest trends along with the performance results, the research identifies the key research gaps, which mainly include the lack of standardized datasets, the challenges of validation of the model, the generalization of the model, and the challenges of scalability. The review ends by identifying the future research directions to develop effective artificial intelligence models for strawberry farming.