An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture

Simple Summary
The future of plant biology, particularly rapidly advancing precision horticulture and predictive breeding, will require the transformation of huge volumes of multi-omics data into structured information and valuable knowledge, representing a key challenge. This review aims to delve into the transformative potential of integrating multi-omics data and artificial intelligence (AI) for a more comprehensive, high-throughput approach to plant phenotyping in horticultural research. We argue that the union of these advanced techniques can provide a robust analytical framework that can handle the complexity of plant biology, thus surmounting the limitations of traditional phenotyping methods. Our discussion also acknowledges the technical and non-technical challenges associated with this integration, critically evaluating their advantages and limitations, proposing potential solutions, and outlining promising future prospects.
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.