Search

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge. This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods. The images are taken from an altitude of approximately 13 ft, with angles between 53 and 58 degrees, under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code and the labeled soybean dataset, offering a valuable resource for future research endeavors in soybean pod counting and related fields. • Created and open-sourced the 1st UAV-acquired image dataset for soybean pod counting. • Pioneered the first study on precise soybean pod counting utilizing UAV images. • Demonstrated SoybeanNet's superiority over 5 state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Key message: The first single dominant resistance gene contributing major resistance to the oomycete pathogen Phytophthora sansomeana was identified and mapped from soybean ‘Colfax’. Phytophthora root rot (PRR) is one of the most important diseases in soybean (Glycine max). PRR is well known to be caused by Phytophthora sojae, but recent studies showed that P. sansomeana also causes extensive root rot of soybean. Depending upon the isolate, it might produce aggressive symptoms, especially in seeds and seedlings. Unlike P. sojae which can be effectively managed by Rps genes, no known major resistance genes have yet been reported for P. sansomeana. Our previous study screened 470 soybean germplasm lines for resistance to P. sansomeana and found that soybean ‘Colfax’ (PI 573008) carries major resistance to the pathogen. In this study, we crossed ‘Colfax’ with a susceptible parent, ‘Senaki’, and developed three mapping populations with a total of 234 F2:3 families. Inheritance pattern analysis indicated a 1:2:1 ratio for resistant: segregating: susceptible lines among all the three populations, indicating a single dominant gene conferring the resistance in ‘Colfax’ (designated as Rpsan1). Linkage analysis using extreme phenotypes anchored Rpsan1 to a 30 Mb region on chromosome 3. By selecting nine polymorphic SNP markers within the region, Rpsan1 was genetically delimited into a 21.3 cM region between Gm03_4487138_A_C and Gm03_5451606_A_C, which corresponds to a 1.06 Mb genomic region containing nine NBS-LRR genes based on Gmax2.0 assembly. The mapping results were then validated using two breeding populations derived from ‘E12076T-03’ × ‘Colfax’ and ‘E16099’ × ‘Colfax’. Marker-assisted resistance spectrum analyses with 9 additional isolates of P. sansomeana indicated that Rpsan1 may be effective towards a broader range of P. sansomeana isolates and has strong merit in protecting soybean to this pathogen in the future. [ABSTRACT FROM AUTHOR]