Application of Grafting Method in Resistance Identification of Sweet Potato Virus Disease and Resistance Evaluation of Elite Sweet Potato [Ipomoea batatas (L.) Lam] Varieties

Hong, Hui; Han, Haohao; Lei, Yayun; Qiao, Hui; Tang, Daobin; Han, Yong‐Sup; Deng, Zhenpeng; Mao, Limin; Wu, Xuli; Zhang, Kai; Wang, Jichun; Lv, Changwen

doi:10.3390/plants12040957

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The study was also aimed at characterizing the two viruses using nucleic acid based techniques in order to add knowledge and enhance an understanding of the variability of these isolates when comparing to others in different geographic regions. Side grafting, symptoms observation, serological, and molecular techniques play a crucial role in screening genotypes for their responses to sweet potato viruses (Huang et al., 2023). Experiments were conducted to screen varieties and breeding lines for possible tolerance to potyviruses and to identify those that can be included in the crossings in sweet potato breeding programs.…”

Section: Introductionmentioning

confidence: 99%

Detection of SPV2 and SPCSV and their possible influence on the yield in multiple virus infections

Mmboyi,

Mulabisana,

Laurie

et al. 2023

Crop Science

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The prevalence of Sweet potato virus 2 (SPV2) and Sweet potato chlorotic stunt virus (SPCSV) in the Limpopo, Mpumalanga and Gauteng provinces of South Africa was determined using serological and nucleic acid based methods. Cloning and sequencing part of the coat protein (CP) gene and the complete genome using Next Generation Sequencing (NGS) were also conducted to characterize and determine the sequence variability in SPV2 isolates. Primers designed in the current study successfully detected and confirmed the prevalence of SPV2 in the three provinces surveyed. The pairwise comparison of the amino acid (aa) sequences encoding the CP gene region confirmed that isolates in this study are related to SPV2, sharing a 94% to 99% CP aa identity with those originally reported in South Africa. The East African (EA) strain of SPCSV was also detected in the three provinces surveyed. Phylogenetic analysis using part of the CP encoding nucleotide (nt) sequences showed that the South African isolates correspond to but are distinct from the other EA strains of SPCSV used as reference sequences. Assessment of varieties and breeding lines’ response to multiple infection of potyviruses identified variety NC1560, breeding lines 2002‐21‐1 and 2006‐5‐4 as promising for inclusion in future breeding programs as sources of parent material for virus tolerance.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%

Detection of SPV2 and SPCSV and their possible influence on the yield in multiple virus infections

Mmboyi,

Mulabisana,

Laurie

et al. 2023

Crop Science

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Feasibility of Detecting Sweet Potato (Ipomoea batatas) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

Zeng,

Ding,

Song

et al. 2023

Agronomy

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The sweet potato is an essential food and economic crop that is often threatened by the devastating sweet potato virus disease (SPVD), especially in developing countries. Traditional laboratory-based direct detection methods and field scouting are commonly used to rapidly detect SPVD. However, these molecular-based methods are costly and disruptive, while field scouting is subjective, labor-intensive, and time-consuming. In this study, we propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images. We proposed a novel object detector called SPVDet, as well as a lightweight version called SPVDet-Nano, using a single-level feature. These detectors were prototyped based on a small-scale publicly available benchmark dataset (PASCAL VOC 2012) and compared to mainstream feature pyramid object detectors using a leading large-scale publicly available benchmark dataset (MS COCO 2017). The learned model weights from this dataset were then transferred to fine-tune the detectors and directly analyze our self-made SPVD dataset encompassing one category and 1074 objects, incorporating the slicing aided hyper inference (SAHI) technology. The results showed that SPVDet outperformed both its single-level counterparts and several mainstream feature pyramid detectors. Furthermore, the introduction of SAHI techniques significantly improved the detection accuracy of SPVDet by 14% in terms of mean average precision (mAP) in both ground and aerial images, and yielded the best detection accuracy of 78.1% from close-up perspectives. These findings demonstrate the feasibility of detecting SPVD from ground and unmanned aerial vehicle (UAV) high-resolution images using the deep learning-based SPVDet object detector proposed here. They also have great implications for broader applications in high-throughput phenotyping of sweet potatoes under biotic stresses, which could accelerate the screening process for genetic resistance against SPVD in plant breeding and provide timely decision support for production management.

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Application of Grafting Method in Resistance Identification of Sweet Potato Virus Disease and Resistance Evaluation of Elite Sweet Potato [Ipomoea batatas (L.) Lam] Varieties

Cited by 2 publications

References 21 publications

Detection of SPV2 and SPCSV and their possible influence on the yield in multiple virus infections

Detection of SPV2 and SPCSV and their possible influence on the yield in multiple virus infections

Feasibility of Detecting Sweet Potato (Ipomoea batatas) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

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