Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 246 This study reviews the current threats and challenges faced by scab, elaborates on the methods and results of high-throughput phenotypic analysis and genome-wide association analysis, and finally explores its impact on future wheat breeding strategies and food security. This study aims to accelerate the identification and application of wheat scab resistance gene loci by combining high-throughput phenotypic analysis with genome-wide association analysis (GWAS), thereby enhancing breeding efficiency and food security. By integrating advanced phenotypic analysis techniques with genomic tools, this study aims to overcome the bottlenecks of traditional resistance evaluation and gene mapping, providing new ideas and resources for resistance breeding. 2 Pathogenesis and Resistance Mechanisms of Fusarium Head Blight in Wheat 2.1 Pathogenic characteristics and epidemic conditions of Fusarium graminearum Once wheat encounters high humidity and high temperature weather during the flowering period, it is very likely to be affected by scab. The "culprit" behind it - Fusarium graminearum- is no new face. This pathogen not only leads to reduced yields but also leaves toxins such as deoxynivalenol (DON) in grains, which are not very friendly to human and animal health. Its spread is not entirely a natural accident. Climate change has contributed to it, and some farming methods have also unintentionally provided it with a "breeding ground", such as no ploughing and corn-to-wheat rotation (Fernando et al., 2020). Once it invades, it gradually breaks through the defense line of wheat by secreting enzymes and effectors. The production of DON further accelerates the progression of the disease. 2.2 Types of resistance in wheat Not all wheat gives up without a fight. Some varieties can resist pathogenic bacteria when they first come into contact, which is called type I resistance. While others, even if infected, can limit the spread of the pathogen within the spike, which belongs to type II resistance (Wu et al., 2022). Of course, there is also the accumulation resistance to toxins such as DON, which is particularly crucial for ensuring food safety. It is worth noting that a single resistance is often insufficient. The varieties that truly stand out are mostly the "result" of the superposition of several resistances. 2.3 Biological basis of resistance traits and phenotypic indicators Ultimately, the resistance display of wheat is still the "collaborative result" of genes and molecules. Some key signaling pathways are activated at the early stage of pathogen invasion. For example, genes involved in pathogen recognition, cell wall reinforcement or toxin detoxification are up-regulated in expression (Dong et al., 2023; Wang et al., 2025; Yang et al., 2025). At the same time, mechanisms such as phenylalanine metabolism, the glutathione cycle, and those related to reactive oxygen species will also be involved (Figure 1). We can observe these resistance manifestations in various ways. Besides the traditional disease grade scoring, molecual-level and metabolomics data also provide a considerable amount of quantifiable evidence. In terms of resistance, "appearance" and "core" are not two separate levels, but rather a whole that reflects each other. 3 High-Throughput Phenotyping Technologies and Their Application in FHB Evaluation 3.1 High-throughput platforms: image analysis, near-infrared spectroscopy (NIRS), and thermal imaging Nowadays, relying on manual visual inspection to assess the resistance to Fusarium head blight (FHB) can no longer keep up with the pace. Image analysis (such as RGB, multispectral, hyperspectral), near-infrared spectroscopy (NIRS), and thermal imaging techniques have gradually become mainstream tools for tracking plant health and identifying disease manifestations (Leiva-Sandoval, 2023). Whether in greenhouses or directly in fields, these systems can be deployed and put into use. Some even incorporate devices like Phenocave, which are low-cost and not complicated to operate, making it easier for breeders and research teams to access these methods that were originally only available in high-end laboratories (Yang et al., 2020). 3.2 Automated phenotypic data collection and disease severity detection algorithms In the past, collecting lesion data was time-consuming and relied on human eye judgment. Now, with the use of automatic acquisition systems and image recognition algorithms, the process has become fast and accurate (Xu and Li, 2022; Jin et al., 2025). Deep learning has been able to identify the subtle changes in grain morphology, and the size of the lesion area can also be quantified very clearly. The most crucial point is that the difference
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