Vincenzo, Cono (2026) Understanding Microbial–Plant–Pathogen interactions in tomato through High-Throughput Plant Phenomics. [Tesi di dottorato]

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Abstract

Intensive tomato production systems are increasingly vulnerable to the recrudescence of soil-borne pathogens. A viable alternative to synthetic fungicides is the use of microbial biological control agents (BCAs), in line with the growing transition toward non-chemical disease management strategies. In this context, a key research objective is the identification and selection of novel and effective microbial antagonists, to be applied either individually or in consortia. High-throughput digital phenotyping technologies offer substantial support for the targeted, non-invasive and rapid screening of novel BCAs. In this thesis, a stepwise plant phenomics-assisted approach is proposed to accelerate the screening of new and effective BCAs aimed at developing microbial consortia for the control of two major tomato soil-borne pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Compared with traditional microbial selection approaches, this pipeline enables a faster and more targeted selection process by employing phenomics as a tool to identify the best-performing microbial isolates. Trichoderma spp. and bacterial strains were evaluated in planta for antagonistic activity against two tomato pathogens, and subsequently screened using a multispectral Dual PlantEye F500 multispectral 3D scanner supported by multivariate analyses. Subsequently, selected and compatible isolates were assembled into microbial consortia sharing a common bacterial core and differing in Trichoderma strain. The microbial consortia exhibited pathogen-specific and time-dependent efficacy, reflecting both differences in pathogen infection strategies and consortium–plant–pathogen interactions. These results showed that high-throughput phenotyping accelerated the microbial selection process in the consortia development and improved reproducibility of the experimental outcomes. Overall, this thesis work demonstrated that phenomics-guided and combined statistical framework enabled robust and powerful approaches for the rational development of effective and sustainable microbial consortia for tomato disease management.

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