<i>Trichoderma</i> Biocontrol Performances against Baby-Lettuce Fusarium Wilt Surveyed by Hyperspectral Imaging-Based Machine Learning and Infrared Thermography

Abstract

Fusarium oxysporum f. sp. lactucae is one of the most aggressive baby-lettuce soilborne pathogens. The application of Trichoderma spp. as biocontrol agents can minimize fungicide treatments and their effective targeted use can be enhanced by support of digital technologies. In this work, two Trichoderma harzianum strains achieved 40–50% inhibition of pathogen radial growth in vitro. Their effectiveness in vivo was surveyed by assessing disease incidence and severity and acquiring hyperspectral and thermal features of the canopies being treated. Infected plants showed a reduced light absorption in the green and near-red regions over time, reflecting the disease progression. In contrast, Trichoderma-treated plant reflectance signatures, even in the presence of the pathogen, converged towards the healthy control values. Seventeen vegetation indices were selected to follow disease progression. The thermographic data were informative in the middle–late stages of disease (15 days post-infection) when symptoms were already visible. A machine-learning model based on hyperspectral data enabled the early detection of the wilting starting from 6 days post-infection, and three different spectral regions sensitive to baby-lettuce wilting (470–490 nm, 740–750 nm, and 920–940 nm) were identified. The obtained results pioneer an effective AI-based decision support system (DSS) for crop monitoring and biocontrol-based management.

Keywords

• digital agriculture
• DSS
• <i>Fusarium oxysporum</i> f. sp. <i>lactucae</i>
• machine learning
• multilayer feed forward artificial neural networks
• precision biological control