Predicting fungal infection sensitivity of sepals in harvested tomatoes using imaging spectroscopy and partial least squares discriminant analysis

• Published in: Journal of near infrared spectroscopy (United Kingdom)
• Main Authors: Bertotto, Mercedes, de Villiers, Hendrik AC, Chauhan, Aneesh, Hogeveen-van Echtelt, Esther, Mensink, Manon, Grbovic, Zeljana, Stefanovic, Dimitrije, Panic, Marko, Brdar, Sanja
• Format: Journal Article
• Language: English
• Published: 03-11-2024
• ISSN: 0967-0335; 1751-6552
• DOI: 10.1177/09670335241292663

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 demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’) were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The 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 were 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, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions.