外文摘要：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.
外文关键词：machine learning;Digital Agriculture;DSS;Fusarium oxysporum f. sp. lactucae;multilayer feed forward artificial neural networks;precision biological control;Trichoderma harzianum
作者：Pallottino, Federico;Costa, Corrado;Ortenzi, Luciano;Manganiello, Gelsomina;Nicastro, Nicola;Pane, Catello
作者单位：Univ Naples Federico II;Tuscia Univ;Consiglio Ric Agr & Anal Econ Agr CREA
期刊名称：AGRICULTURE-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台