Manganiello Gelsomina, Nicastro Nicola, Caputo Michele, Zaccardelli Massimo, Cardi Teodoro, Pane Catello
Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria, Centro di ricerca Orticoltura e Florovivaismo, Pontecagnano Faiano, Italy.
Front Plant Sci. 2021 Feb 24;12:630059. doi: 10.3389/fpls.2021.630059. eCollection 2021.
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 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 spp. strains were characterized both and for their ability to contain and development. All spp. showed, significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with and . The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to and 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--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.
研究越来越多地聚焦于筛选新型且有效的生物防治剂(BCAs)来对抗土传植物病原体。BCAs的大规模应用需要快速且可靠的筛选方法来评估大量候选物的功效。在此背景下,数字技术不仅可用于早期病害检测,还可用于BCAs的快速性能分析。本研究调查了不同种的能力,以抑制主要叶菜类蔬菜病原体的发展,并应用功能性植物成像来选择针对多种病理系统表现最佳的拮抗剂。具体而言,对16种不同的菌株进行了和的表征,以确定它们抑制和发展的能力。所有菌株均显示出对目标植物病原体的显著径向生长抑制。此外,分别对感染和的野生火箭菜、绿色和红色小叶生菜进行了生物防治试验。通过高光谱成像监测植物状态。属于和种的两个菌株Tl35和Ta56,在这三种病理系统中显著降低了发病率和病情严重程度(DI和DSI)。根据从植物 - 病原体相互作用图像中提取的高光谱数据计算得出的植被指数,被证明适合用于反映植物健康状况。其中四个(OSAVI、SAVI、TSAVI和TVI)对所有分析的病理系统都具有信息价值,根据健康、感染和生物保护植物之间光谱特征的显著变化,它们与DSI密切相关。研究结果清楚地表明,通过将数字植物成像作为BCAs有效性的大规模筛选方法和精确生物防治支持手段,有可能促进作物的可持续病害管理。