Effect of Alternaria and Septoria spot on sunflower yield

Brand, Silvane Isabel; Heldwein, Arno Bernardo; Radons, Sidinei Zwick; Maldaner, Ivan Carlos; Hinnah, Fernando Dill; Guse, Fabrício Ivan; Silva, Jocélia Rosa da

doi:10.1007/s00484-020-02006-8

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Although not among the most popular crops in Brazil, where annual grasses and leguminous crops dominate, sunflower serves as an alternative for crop rotation systems. Water stress and diseases are the primary factors contributing to yield reduction in this crop (Loose, Heldwein, Silva, & Bortoluzzi, 2019;Brand et al, 2020), with a national average yield of approximately 1,143 kg ha -1 (Companhia Nacional de Abastecimento [CONAB], 2022). Such a stress leads to morphophysiological changes in cultivated plants as they adapt to new conditions (Loose et al, 2019;Ni et al 2019;Masalia, Temme, Torralba, & Burke, 2018).…”

Section: Introductionmentioning

confidence: 99%

Sunflower root growth and distribution under varied water regimes in two edaphoclimatic conditions

Loose,

Heldwein,

Silva

et al. 2024

Acta Sci. Agron.

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Sunflower growth is adversely impacted by both excess and insufficient water. Research on root growth in this crop under water stress conditions remains limited and does not fully elucidate the plant's response to varying soil and climatic conditions. This study aimed to determine root growth, depth, and distribution of sunflower plants under different water stress conditions, such as deficit or excess, in two soil classes and sown during two distinct periods. Experiments were conducted after sowing at the beginning of September (first crop season) and at the beginning of January (second crop season) in an Ultisol (Santa Maria, Rio Grande do Sul State, Brazil) and an Oxisol (Panambi, Rio Grande do Sul State, Brazil). Water condition treatments applied from stage V6 included control, water deficit, and water excess. Roots were collected using an auger drill during the first crop season. The variables analyzed comprised root length density and accumulated root within the soil profile. During the second crop season, sunflower roots were visually assessed in the soil profile after trench excavation. Root system depth and root dry mass were evaluated during both sowing periods. Results indicated that sunflower root penetration is deeper in Ultisol than in Oxisol. Water deficit promotes root depth, while water excess promotes root growth near the surface. Sowing during the first crop season results in deeper root penetration and higher root dry mass production compared to the second crop season.

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Section: Introductionmentioning

confidence: 99%

Sunflower root growth and distribution under varied water regimes in two edaphoclimatic conditions

Loose,

Heldwein,

Silva

et al. 2024

Acta Sci. Agron.

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“…76% of the world's production [4]. However, the leaves of the plant usually harbor a diverse microbiota, and harmful floating pathogens can cause recurrent disease epidemics that threaten crop quality and yield [5][6][7][8]. Therefore, accurate detection and implementation of appropriate preventive measures at the early stage of disease can effectively limit the frequency and spread of leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

TeenyNet: a novel lightweight attention model for sunflower disease detection

Zhong,

Tong

2023

Meas. Sci. Technol.

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Sunflower is one of the oilseed crops which is popularly and widely cultivated globally and contributes significantly to human health. Leaf diseases of sunflower seriously affect the growth and yield of sunflower, which directly leads to the loss of agricultural economy. However, existing machine learning algorithms and deep learning techniques are mainly based on large models with attention mechanisms, which lack considerations in computational performance, especially model size. Therefore, this study proposes a lightweight model called TeenyNet to break through the dilemma. First, the designed global multi-frequency feature extraction module decomposes the image to extract multi-frequency multi-scale features. Then, a parameter-free maximum pooling layer further extracts edge and texture features and simplifies the network complexity through downsampling, after which the proposed lightweight dual fusion attention and multi-branching structure fuses all the feature vectors to enhance multidimensional feature learning and accelerate the model convergence. Finally, the fully connected linear layer solves the multi-classification problem of sunflower disease under natural illumination background conditions. The experimental results show that TeenyNet obtains the highest accuracy of 98.94% for sunflower disease recognition with a minimum size of 143 KB and has better recognition performance in comparison experiments. TeenyNet can be effectively used for the detection of sunflower leaf diseases to achieve disease prevention and control.

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Morphological and Molecular Characterization of Bacterial Pathogens Associated with Leaf Mottle of Sunflower in Northern Kazakhstan

Kiyan,

Smagulova,

Kukhar

et al. 2024

Plant Disease

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Leaf mottle is a serious disease in the growth and development of sunflowers (Helianthus annuus L.) that can affect their quality and yield. Over the past years, the North Kazakhstan region, a sunflower-producing area in Kazakhstan, has been seriously affected by leaf mottle. Since 2021, symptomatic leaves have been collected from production areas of this base to determine the pathogens of sunflower foliar diseases. One hundred bacterial strains were isolated, and 2 genera and 5 species were identified based on morphological characteristics, molecular genetics and phylogenetic analysis (16S gene region). The genus Bacillus was represented by 4 species-B. subtilis, B. megaterium, B. amyloliquefaciens and B. flexus. The genus Paenibacillus is represented by one species, P. peoriae. Pathogenicity experiments showed that B. subtilis, B. megaterium, B. flexus and P. peoriae could cause disease symptoms. However, B. flexus leaves are highly similar to infected leaves observed under natural conditions in the field. Therefore, these bacterial isolates are considered to be the primary pathogens causing sunflower leaf mottle, and B. flexus was the most common and virulent pathogen in this study. In addition, this is the first study of B. megaterium, B. flexus and P. peoriae as pathogens associated with sunflower leaf mottle in Kazakhstan.

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Effect of Alternaria and Septoria spot on sunflower yield

Cited by 7 publications

References 13 publications

Sunflower root growth and distribution under varied water regimes in two edaphoclimatic conditions

Sunflower root growth and distribution under varied water regimes in two edaphoclimatic conditions

TeenyNet: a novel lightweight attention model for sunflower disease detection

Morphological and Molecular Characterization of Bacterial Pathogens Associated with Leaf Mottle of Sunflower in Northern Kazakhstan

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