Evidence based disease control methods in potato production: a systematic map protocol

Vilvert, Elisa; Stridh, Linnea; Andersson, B.; Olson, Åke; Aldén, Louise; Berlin, Anna

doi:10.1186/s13750-022-00259-x

Cited by 25 publications

(11 citation statements)

References 25 publications

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“…The method was carried out automatically using the database's sorting function. The research question produced in the preceding procedure served as the basis for the selection standard [39], [40]. Then, the publications were collected between 2017 and 2021, and only English-language published articles were chosen from IEEE, Scopus, and WOS.…”

Section: Screeningmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

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Section: Screeningmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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“…It has been suggested that growing potatoes will ensure future food security because of their remarkably high adaptive capacity and short life span, giving efficient yields per cultivation [4,25,[39][40][41][42][43]. Figure 4 presents the percentage distribution of global potato production by continent in 2018.…”

Section: Global Production Of Potatoesmentioning

confidence: 99%

Climate change impacts on global potato yields: a review

Adekanmbi,

Wang,

Basheer

et al. 2023

Environ. Res.: Climate

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Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO2), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.

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“…Potato ( Solanum tuberosum ), a key crop for human consumption, has been widely planted to meet expanding demand (Vilvert et al, 2022). China leads the world in total potato planting area and production (Li et al, 2022), and agro‐pastoral ecotone (APE) is the primary production base, with a planting area of 400,000 ha (Tang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Isolation and identification of Epicoccum nigrum as the causal agent of brown spot disease in Solanum tuberosum in China

et al. 2023

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Brown spots occurring on potato leaves affect the normal growth of host plants. Recently, a new brown spot disease with some black dots was discovered on potato leaves in Dingxi, China. To confirm the causal agents of potato brown spots, the diseased lesions were collected and disinfected, and only one type of isolate, with football‐shaped conidia, was obtained from disinfected diseased leaves in three experimental fields. Pathogenicity tests of four representative isolates revealed that they can cause disease symptoms on 50%–75% of wounded and non‐wounded potato leaves. In addition, a typical isolate, DXPH, was identified based on morphological and molecular characteristics, and the colony and conidial characteristics were similar to those of Epicoccum sp. Multilocus sequence analysis showed that the internal transcribed spacer (ITS), RNA polymerase II second largest subunit (RPB2) and β‐tubulin (Tub2) genes of DXPH were 100%, 100% and 99% identical to the sequences of Epicoccum nigrum (CBS 140523), respectively. Additionally, antagonistic evaluation confirmed that the representative pathogenic strain DXPH showed greatest inhibition of the mycelial growth of Colletotrichum coccodes and Fusarium chlamydosporum in vitro, with antagonistic effects of 66.67% and 61.56%, respectively. Our findings provide evidence that E. nigrum can be pathogenic to potatoes in vivo in China, while retaining a biocontrol capacity on C. coccodes, Fusarium spp. and Botrytis cinerea in vitro, which will be helpful for the effective control of potato brown spot disease and management of the use of E. nigrum as a biological control agent.

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Evidence based disease control methods in potato production: a systematic map protocol

Cited by 25 publications

References 25 publications

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Climate change impacts on global potato yields: a review

Isolation and identification of Epicoccum nigrum as the causal agent of brown spot disease in Solanum tuberosum in China

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