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<p>Leaf blight disease, caused by <i>Nigrospora sphaerica</i>, is the most significant disease of groundnut. Groundnut is an important legume in rural Zimbabwe. The absence of a&#xa0;standardised tool for disease phenotyping has hindered precision for disease monitoring in Zimbabwe. Key challenges remain: lack of automation, accuracy and precision. Proximal RGB imaging can address these issues via phytopathometry. Phytopathometry uses standard area diagrams (SADs) to quantify diseases. In this study, we phenotyped groundnut characteristics using both proximal RGB imaging and a&#xa0;recently developed SAD. The goal was to select groundnut landrace genotypes tolerant to leaf blight. A&#xa0;linear regression model trained on RGB indices produce a&#xa0;high coefficient of determination (R<sup>2</sup> = 0.92). A&#xa0;logistic regression model using SAD ordinal data showed high accuracy (0.88). The landrace genotype CHIGMR001 had a&#xa0;greater green area (GA &gt; 0.3) and lower disease severity than the check varieties. Broad sense heritability (H<sup>2</sup>B) was high for both disease severity (H<sup>2</sup>B = 77%) and green area (H<sup>2</sup>B = 0.72%). A&#xa0;multi-trait selection index for RGB indices and ordinal variable superiority index ranked KASMT100 as the best disease-tolerant. In conclusion, data showed that combining the latest SAD for groundnut phytopathogens with RGB indices can effectively phenotype leaf blight disease in groundnuts.</p>

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Low-Cost RGB-Imaging and Standard Area Diagram Enables Phenotyping and Indirect Selection for Leaf Blight Disease Tolerance in Groundnut

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Hardlife Chinwa,
  • Casper Nyaradzai Kamutando,
  • Martin Sanyamuwera,
  • Juliet Murimwa,
  • Kelvin Chipoyi,
  • Mashoko Stephen Grey,
  • Regis Chikowo,
  • Elizabeth Ngadze

摘要

Leaf blight disease, caused by Nigrospora sphaerica, is the most significant disease of groundnut. Groundnut is an important legume in rural Zimbabwe. The absence of a standardised tool for disease phenotyping has hindered precision for disease monitoring in Zimbabwe. Key challenges remain: lack of automation, accuracy and precision. Proximal RGB imaging can address these issues via phytopathometry. Phytopathometry uses standard area diagrams (SADs) to quantify diseases. In this study, we phenotyped groundnut characteristics using both proximal RGB imaging and a recently developed SAD. The goal was to select groundnut landrace genotypes tolerant to leaf blight. A linear regression model trained on RGB indices produce a high coefficient of determination (R2 = 0.92). A logistic regression model using SAD ordinal data showed high accuracy (0.88). The landrace genotype CHIGMR001 had a greater green area (GA > 0.3) and lower disease severity than the check varieties. Broad sense heritability (H2B) was high for both disease severity (H2B = 77%) and green area (H2B = 0.72%). A multi-trait selection index for RGB indices and ordinal variable superiority index ranked KASMT100 as the best disease-tolerant. In conclusion, data showed that combining the latest SAD for groundnut phytopathogens with RGB indices can effectively phenotype leaf blight disease in groundnuts.