Applications of AI and Computational Biology in Horticultural Science

Abstract

Horticulture, a critical sector of global agriculture, faces mounting challenges from climate change, resource scarcity, and the increasing demand for sustainable food production. Traditional methods, particularly in phenotyping and breeding, have become a bottleneck for progress. This chapter explores the transformative role of artificial intelligence (AI) and computational biology in revolutionizing horticultural science. We provide a comprehensive overview of core AI technologies – including machine learning, deep learning, computer vision, and the Internet of Things (IoT) – and their specific applications in high-throughput phenotyping, precise disease and pest detection, yield prediction, automated harvesting, and postharvest management. Concurrently, we delve into the field of computational biology, detailing how genomics, transcriptomics, proteomics, and metabolomics (multi-omics) are being leveraged to decode the genetic and molecular basis of complex horticultural traits. A central theme of this chapter is the powerful integration of AI with multi-omics data, which is enabling the development of predictive models for precision breeding, stress resilience, and nutritional improvement. We also discuss the emerging importance of Explainable AI (XAI) in translating model predictions into actionable biological insights for farmers and researchers. While highlighting groundbreaking applications and real-world case studies, the chapter addresses the current limitations, ethical considerations, and data-related challenges. Finally, we outline future directions, concluding that the synergistic convergence of AI and computational biology is paving the way for a new era of data-driven, efficient, and sustainable horticulture capable of meeting the demands of the 21st century.