Molecular Plant Breeding 2024, Vol.15, No.1, 15-26 http://genbreedpublisher.com/index.php/mpb 17 Schaeffer and Nakata (2015) discussed the evolution of CRISPR/Cas9 from conceptual validation studies to applications in crop trait improvement, highlighting the need for crop-specific vectors and transformation protocols. Xu et al. (2015) described the use of CRISPR/Cas9-mediated genome editing in rice, focusing on the modification of genetic and “non-transgenic” target genomes. Ding et al. (2016) provided insights into the latest developments and applications of CRISPR/Cas9 in plant research, discussing the establishment of gene knockout in various plant species and how it can be used for specific mutation/integration and transcriptional control of target genes. Together, these studies demonstrate the revolutionary impact of the CRISPR-Cas9 system in plant breeding, providing new approaches for genome editing and trait improvement. 2.3 High-throughput phenotypic analysis High-throughput phenotypic analysis is one of the key methods in Breeding 4.0. By utilizing advanced sensor technology, imaging technology, and automated systems, breeders can efficiently measure and analyze multiple traits of crops, including growth characteristics, physiological indicators, and yield-related traits. High-throughput phenotypic analysis provides high-resolution and large-scale trait data, offering breeders a more accurate basis for crop evaluation and selection. Early research in high-throughput phenotypic analysis laid the foundational theoretical and technical support for modern plant breeding, driving the development of this field. The molecular technology development in plant breeding, such as the application of RAPDs and microsatellite markers, provided new methods, especially in DNA separation, gene amplification, and data processing automation (Rafalski and Tingey, 1993). Utilizing genomic strategies has accelerated gene discovery, and combining high-throughput transformation processes with automated analysis methods has provided new avenues for improving plant quality (Mazur et al., 1999). Comparative genomics research has advanced the adaptability of plants to stress, particularly by using high-throughput phenotypic analysis techniques, laying the foundation for the effectiveness of engineering strategies (Cushman and Bohnert, 2000). The development of DNA sequencing technology, especially the study of the global pattern of gene expression, has brought a revolutionary transformation in plant biology, holding significant importance in the field of plant breeding (Harmer and Kay, 2000). 2.4 Data analysis and artificial intelligence Breeding 4.0 utilizes data analysis and artificial intelligence to process and interpret large-scale genetic and phenotypic data. By applying machine learning, deep learning, and statistical models, breeders can extract valuable information from massive datasets, identify crucial genetic factors and trait associations, and predict the breeding potential of crops. The technologies of data analysis and artificial intelligence play a crucial role in decision-making and strategy formulation of breeding, enhancing breeding efficiency and accuracy. Early plant breeding research primarily relied on traditional methods but gradually started incorporating statistical models and computational approaches to optimize the breeding process and gain deeper insights into the characteristics of different varieties. Donald (1968) proposed a plant breeding approach based on model features in his paper “Breeding for Ideotypes of Crop Plants”, where these features influence photosynthesis, growth, and (in cereals) grain production. Shorter et al. (1991) investigated the role of breeding, physiology, and models in assessing the adaptation of plant genotypes to target environments. Smith et al. (1997) explored how models and statistical methods could be used to evaluate different varieties’ responses to biological control agents, thereby improving the effectiveness of biological control. Goldman (1999) discussed the method of using Wisconsin Fast-growing Plants in a cycle selection process, which holds significance for education and research.
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