Adaptability and stability of corn inbred lines regarding resistance to gray leaf spot and northern leaf blight

Saito, Belisa Cristina; Silva, Leonardo Queiroz; Andrade, João Antônio da Costa; Goodman, M. M.

doi:10.1590/1984-70332018v18n2a21

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The typical NCLB symptoms manifest as gray-green, long fusiform, water-soaked lesions on young leaves and later developed into larger necrotic lesions ( Vieira et al, 2014 ; Shi et al, 2017 ). In susceptible corn cultivars, these necrotic lesions expand and merge, causing extensive damage to the majority of leaves, thereby markedly weakening the photosynthetic capacity of corn plants ( Saito et al, 2018 ; Kotze et al, 2019 ). Although outbreaks of NCLB occur worldwide, and in susceptible corn cultivars can result in severe yield losses of more than 60%, these outbreaks tend to be most frequent in low latitude regions characterized by temperate and humid conditions ( Dingerdissen et al, 1996 ; Tefferi et al, 1996 ; Ferguson and Carson, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Differentiation and Mixed Reproductive Strategies in the Northern Corn Leaf Blight Pathogen Setosphaeria turcica From Sweet Corn in Fujian Province, China

et al. 2021

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The northern corn leaf blight (NCLB) pathogen Setosphaeria turcica (Luttrell) Leonard and Suggs is one of the main biotic constraints on sweet corn (Zea mays L.) yield and quality in Fujian Province, China. Currently, however, there is comparatively little information available regarding the distribution of mating types, population genetics, and reproductive strategies of this pathogen in Fujian. In this study, we investigated the distribution of mating types and population genetics of 117 isolates of S. turcica collected from seven of the main sweet corn-growing regions in Fujian Province, based on multiple polymerase chain reaction analyses using two mating type-specific primer pairs and 11 inter-simple sequence repeat markers. Furthermore, we examined the mode of reproduction of Fujian S. turcica populations. Both MAT1-1 and MAT1-2 mating types were detected throughout all seven sampling locations. The majority of MAT1-2 isolates were detected from Dongyou, Jian’ou, Pingnan, Songxi, and Longyan, whereas a large proportion of the detected MAT1-1 isolates were among those collected from Dongfeng and Nanjing. Furthermore, we detected five shared multi-locus haplotypes among S. turcica isolates from Dongyou, Jian’ou, Pingnan, Nanjing, and Songxi, whereas no shared haplotypes were observed between the Dongfeng (or Longyan) population and these five populations. Pairwise comparisons of the indices ΦPT and Nm, and population structure and principal coordinate analyses indicated genetic differentiation between both the regional and the mating type populations of S. turcica in Fujian. The skewed mating type ratio associated with low a haplotypic diversity and evident linkage disequilibrium reveals a mixed reproductive strategy for S. turcica populations in Fujian Province. The findings of this study advance our current understanding of the genetic diversity, population structure, and reproductive strategies of S. turcica populations infecting sweet corn in Fujian Province, and will potentially contribute to further resistance breeding efforts.

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Section: Introductionmentioning

confidence: 99%

Genetic Differentiation and Mixed Reproductive Strategies in the Northern Corn Leaf Blight Pathogen Setosphaeria turcica From Sweet Corn in Fujian Province, China

et al. 2021

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“…This article will demonstrate their efficiency and why they work from three perspectives, namely, theoretical derivation, experiment, and visualization. We collected maize leaf diseases datasets with complex backgrounds in our experimental field and proposed an improved model based on ViT and CCT to classify maize into four categories ( Figure 2 ), namely, healthy (H), southern corn leaf blight (SCLB) ( Aregbesola et al, 2020 ), gray leaf spot (GLS) ( Saito et al, 2018 ), and southern corn rust (SR) ( Wang S. et al, 2019 ). The model outperforms some mainstream CNNs compared with it in all metrics, with a smaller number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

et al. 2022

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Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.

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“…Genet et al (2017) used testers and Afekhai et al (2017) based this estimation on the use of the North Carolina mating design II. Furthermore, recurrent selection has also been used to improve performance of SVs (Baktash 2016) and Saito et al (2018) considering adaptability and stability, found five corn lines suitable to produce dent synthetics resistant to gray leaf spot and northern leaf blight.…”

Section: Introductionmentioning

confidence: 99%

Exact inbreeding coefficient of maize synthetic varieties derived from mixed lines and single crosses

Arellano-Suarez

Rodríguez-Pérez

Peña-Lomelí

et al. 2020

Crop Breed. Appl. Biotechnol.

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Adaptability and stability of corn inbred lines regarding resistance to gray leaf spot and northern leaf blight

Cited by 6 publications

References 19 publications

Genetic Differentiation and Mixed Reproductive Strategies in the Northern Corn Leaf Blight Pathogen Setosphaeria turcica From Sweet Corn in Fujian Province, China

Genetic Differentiation and Mixed Reproductive Strategies in the Northern Corn Leaf Blight Pathogen Setosphaeria turcica From Sweet Corn in Fujian Province, China

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

Exact inbreeding coefficient of maize synthetic varieties derived from mixed lines and single crosses

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