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There is a great demand for dragon fruit in China and Southeast Asia. Manual picking of dragon fruit requires a lot of labor. It is imperative to study the dragon fruit-picking robot. The visual guidance system is an important part of a picking robot. To realize the automatic picking of dragon fruit, this paper proposes a detection method of dragon fruit based on RDE-YOLOv7 to identify and locate dragon fruit more accurately. RepGhost and decoupled head are introduced into YOLOv7 to better extract features and better predict results. In addition, multiple ECA blocks are introduced into various locations of the network to extract effective information from a large amount of information. The experimental results show that the RDE-YOLOv7 improves the precision, recall, and mean average precision by 5.0%, 2.1%, and 1.6%. The RDE-YOLOv7 also has high accuracy for fruit detection under different lighting conditions and different blur degrees. Using the RDE-YOLOv7, we build a dragon fruit picking system and conduct positioning and picking experiments. The spatial positioning error of the system is only 2.51 mm, 2.43 mm, and 1.84 mm. The picking experiments indicate that the RDE-YOLOv7 can accurately detect dragon fruits, theoretically supporting the development of dragon fruit-picking robots. [ABSTRACT FROM AUTHOR]

Waxy maize (Zea mays L. var. ceratina) is a special type of maize characterized by a sticky texture when cooked, due to high amylopectin content in the endosperm. Waxy maize is popular in China and Southeast Asia for fresh consumption. Breeding strategies have been used to improve the quality of waxy maize, including hybrid breeding by crossing super sweet maize and waxy maize. However, the lack of a marker has limited the efficiency of breeding for the waxy trait, especially because the waxy allele is recessive. In this study, we conducted a genome-wide association study (GWAS) in an association panel consisting of 213 inbred lines and recombinant inbred lines (RILs) of field maize and waxy maize to identify loci associated with the waxy kernel phenotype. The genotypic data were 155,768 SNPs derived from the high-density 600 K maize genotyping array for single-nucleotide polymorphisms (SNPs). The GWAS results identified the qWx9 locus on chromosome 9 (25.06–25.18 Mb) associated with the trait. Based on the most significantly associated SNP (AX-90613979, −log10(P) = 6.8)), which was located on Wx1, a MassArray marker was developed and validated in a panel of 139 maize lines containing waxy maize and sweet maize with different amylose content. The newly developed marker had a significant association with amylose content (R2 value of 0.81, p < 0.001) and clearly distinguished between waxy maize and sweet maize lines that had different amylose content. This marker will be useful for maize breeding programs for the waxy trait, as well as for breeding programs for hybrid maize combining the sweetness and waxy traits. The gene-based SNP markers could aid breeders by eliminating the costs and time required to perform lengthy field trials and help to accelerate sweet maize and waxy maize breeding programs. [ABSTRACT FROM AUTHOR]