Deep Learning

Abstract

This chapter provides a comprehensive overview of the transformative role of deep learning (DL) in plant biology and agriculture. It begins by establishing the foundational concepts of DL and examining the confluence of factors, including big data availability and advancements in computational power, that have propelled its recent ascendancy. The discussion then delves into the technical evolution of DL models, highlighting the impact of architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, alongside the enabling technologies such as high-performance computing, advanced sensing, and Internet of Things (IoT). While acknowledging the significant progress, the chapter also addresses the inherent challenges in DL implementation, particularly concerning data quality, computational intensity, model interpretability, and generalizability. The core of the chapter explores the diverse applications of DL across key domains. In plant biology, DL is revolutionizing genomics and transcriptomics through enhanced variant calling, gene prediction, and genomic selection, while also enabling high-throughput phenotyping (HTP) and precise trait prediction for accelerated plant breeding. In agriculture, DL applications extend to precision agriculture for optimized crop monitoring and resource management, enhancing the efficiency and resilience of agricultural supply chains, and contributing to environmental monitoring and sustainability efforts. Ultimately, the chapter outlines emerging trends such as more sophisticated architectures, edge computing, and interdisciplinary problem-solving, alongside a candid discussion of remaining challenges, including data scarcity, model transparency, and robustness in dynamic environments. This synthesis underscores (or stresses) DL’s indispensable role in fostering sustainable agricultural practices and ensuring global food security by bolstering and improving crop breeding strategies.