Agriculture is the source of human food ingredients and the foundation for survival and development. Modern agriculture meets the demand for sufficient food, it has also led to diet-related diseases such as hyperlipidemia, hypertension, and hyperglycemia due to unbalanced diets. After the “Second International Conference on Nutrition (ICN2)” jointly held by the FAO and WTO in 2014, functional agriculture research aimed at meeting people's nutritional and health needs rapidly developed both domestically and internationally, China has issued guiding documents such as the “National Nutrition Plan (2017-2030)” and the “Healthy China 2030” planning outline. This paper systematically reviews the background of functional agriculture emergence and focuses on the functionalization of staple crops to discuss the research progress in functional agriculture of the world. It also summarizes the key research areas of functional agriculture, including exploration of functional components in germplasm resources, breeding of new functional varieties, agronomic enhancement measures, and development and promotion of health foods. On this basis, in accordance with the requirements of the “China Food and Nutrition Development Outline (2025-2030)” proposed by the Ministry of Agriculture and Rural Affairs, the National Health Commission, and the Ministry of Industry and Information Technology in February 2025, four suggestions are put forward: Strengthening top-level government design, establishing major projects for staple crop functionalization, accelerating the construction of standard systems, and improving intellectual property protection for varieties. These suggestions aim to provide theoretical support and practical paths for implementing China large-scale agriculture and grain strategy and ensuring national nutrition and health. This paper offers valuable insights for establishing a functional agricultural industry system with Chinese characteristics.

The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30−5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN.caas-4A2, QSN.caas-4D and QSN.caas-5B2, were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN.caas-4A2 and QSN.caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.

Black point is a black discoloration of the grain embryo that reduces the grain quality and commodity grade. Identifying the underlying genetic loci can facilitate the improvement of black point resistance in wheat. Here, 262 recombinant inbred lines (RILs) from the cross of Zhongmai 578/Jimai 22 were evaluated for their black point reactions in five environments. A high-density genetic linkage map of the RIL population was constructed with the wheat 50K single nucleotide polymorphism (SNP) array. Six stable QTLs for black point resistance were detected, QBp.caas-2A, QBp.caas-2B1, QBp.caas-2B2, QBp.caas-2D, QBp.caas-3A, and QBp.caas-5B, which explained 2.1–28.8% of the phenotypic variances. The resistance alleles of QBp.caas-2B1 and QBp.caas-2B2 were contributed by Zhongmai 578 while the others were from Jimai 22. QBp.caas-2B2, QBp.caas-2D and QBp.caas-3A overlapped with previously reported loci, whereas QBp.caas-2A, QBp.caas-2B1 and QBp.caas-5B are likely to be new. Five kompetitive allele-specific PCR (KASP) markers, Kasp_2A_BP, Kasp_2B1_BP, Kasp_2B2_BP, Kasp_3A_BP, and Kasp_5B_BP, were validated in a natural population of 165 cultivars. The findings of this study provide useful QTLs and molecular markers for the improvement of black point resistance in wheat through marker-assisted breeding.