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Tomatoes (Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a need for a dependable, non-destructive method capable of swiftly predicting susceptibility to fungal infection. Our objective is to utilize Hyperspectral Imaging (HSI) with chemometric analysis to achieve this, a novel approach not previously explored in research. In our study, we employed three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’). Hyperspectral images were captured on May 10th, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on May 12th and 13th, averaging severity scores assigned per sepal. Our methodology involved extracting spectra from HSI images and calibrating and validating models using Partial Least Squares Discriminant Analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models are categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, our research suggests that distinguishing between more and less susceptible sepals is feasible under controlled conditions.

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Predicting Fungal Infection Sensitivity of Sepals in Harvested Tomatoes Using Imaging Spectroscopy and Partial Least Squares Discriminant Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Mercedes Bertotto,
  • Hendrik de Villiers,
  • Aneesh Chauhan,
  • Esther Hogeveen-van Echtelt,
  • Manon Mensink,
  • Zeljana Grbovic,
  • Dimitrije Stefanovic,
  • Marko Panic,
  • Sanja Brdar

摘要

Tomatoes (Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a need for a dependable, non-destructive method capable of swiftly predicting susceptibility to fungal infection. Our objective is to utilize Hyperspectral Imaging (HSI) with chemometric analysis to achieve this, a novel approach not previously explored in research. In our study, we employed three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’). Hyperspectral images were captured on May 10th, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on May 12th and 13th, averaging severity scores assigned per sepal. Our methodology involved extracting spectra from HSI images and calibrating and validating models using Partial Least Squares Discriminant Analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models are categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, our research suggests that distinguishing between more and less susceptible sepals is feasible under controlled conditions.