Digital Twins for Fruit Cultivation: A Systematic Literature Review

Digital twin (DT) technology is increasingly recognized as a transformative tool in precision agriculture, yet its application to fruit cultivation remains fragmented and poorly systematized. This paper presents a systematic literature review of DT applications in fruit cultivation, following the PRISMA 2020 methodology. A structured search across Scopus and Web of Science databases yielded 38 studies published between 2019 and 2025 for review. Results revealed a strong concentration of research in the areas of sensing and Internet of Things, Cold Chain and Postharvest, and Modeling and Simulation. Also, it is indicated that DTs in this domain remain largely at the laboratory validation stage. Switzerland, the United States, and China lead in research output, while strawberries, oranges, and apples were the most studied crops. Key challenges identified included limited data availability, poor model transferability across cultivars, high implementation complexity, and insufficient integration across supply chain stages. This review highlights the need for standardized frameworks, open datasets, and cross-stage DT architectures to accelerate the transition from research prototypes to operational deployment in fruit cultivation.