The Digital Orchard: Advanced Data-Driven Technologies in Apple Breeding and Genetic Modification

Abstract

The apple (Malus × domestica), a globally significant perennial fruit crop, facesimmense pressure from climate change, evolving pathogens, and consumerdemand for novel traits. Also, remains constrained by slow trait selection despitetechnological advances. Further, the traditional breeding methods are slow andresource-intensive, hampered by the apple’s long juvenile period and highheterozygosity. This systematic literature review (SLR) synthesizes the state ofthe art in advanced data-driven technologies for accelerating apple breeding andgenetic modification. Following the PRISMA-EcoEvo protocol, 47 selectedstudies were analyzed from databases including Web of Science, Scopus, andPubMed. Our thematic synthesis reveals a paradigm shift towards a“digitalbreeding”model, characterized by the convergence of three coretechnological pillars. First, high-throughput phenotyping (HTP), whichleverages sensor modalities such as RGB-D, hyperspectral imaging, and LiDAR,is automating the collection of trait data at an unprecedented scale. Second,machine learning (ML) and deep learning (DL) algorithms are being deployed fordiverse applications, including cultivar identification with over 96% accuracy,non-destructive quality prediction, and genomic selection, thereby boostingpredictive ability for key traits by up to 18%. Third, precise and efficientgenome editing, predominantly using Clustered Regularly Interspaced ShortPalindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), is enablingthe rapid introduction of desirable traits, such as disease resistance, enhancedshelf life, and improved nutrient uptake. Demonstrated transgene-free editingprotocols are accelerating the path to commercialization. We further explore theintegration of these pillars through the agricultural internet of things (AIoT) anddiscuss emerging frontiers, including federated learning for data privacy,explainable AI (XAI) for model transparency, and the implications of recentregulatory frameworks. This review identifies critical research gaps, including the need for standardized open-access datasets and integrated end-to-endsystem validation. It concludes that the synergistic application of thesetechnologies is poised to revolutionize the speed, precision, and resilience ofapple improvement programs worldwide.