Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables.

Abstract

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii . The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant- Trichoderma -pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support.

Plant phenotyping relevance

ハイパースペクトル画像と植生指数を用いて植物の健康状態・病害重症度を推定し、BCAsの大規模スクリーニング手法として評価しており、表現型取得法が中心である。

Code and data availability

The paper's hyperspectral images, vegetation-index datasets, and R analysis code are not deposited in any public repository. The only data availability statement covers GenBank ITS/TEF1 sequences, which are molecular omics deposits and thus excluded as paper-specific phenotyping assets. No author code, image, or trait/

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