Observational detection methods for outdoor ornamental plant diseases

In order to ensure todayRSQUOs intensive competitive horticulture, especially in nurseries, it is important to monitor the cultivation process and conditions to introduce operational care adjustments based on data analysis. In contemporary conditions, monitoring processes for individual plants is primarily possible in a controlled environment in labs and greenhouses. Outdoors, these monitoring processes develop slowly, especially in the ornamental plant sector because of the wide assortment of cultivars and very small nursery specialization. To enable an outdoor monitoring process, the project LSQUOAutonomous robotic platform Latvian i-gardenRSQUO has been implemented since 2019. The aim of the project is to follow the development of plants and detect damage made by pathogens during inoculation to preserve survivability. In order to achieve the set goal – to detect the disease at an early stage NDASH a remote multispectral plant photography method was used. The aim of the project is to draw up and implement in practice a robotic, autonomous platform Latvijas iDārzs (Latvian i-Garden) that will ensure a plant monitoring and tending function as well as automation and digitalization of the production process. During the monitoring, the data were obtained from perennials planted in specially designed plant beds. Seven plant varieties – Achillea ‘Desert Eva Red’, Aster novi-belgii ‘Herbstgruss vom Bresserhof”, Astilbe ‘Bronzelaub’, Echinacea ‘Primadonna Deep Rose’, Heuchera micrantha ‘Palace Purple’, Hosta hybrida ‘Fragrant Blue’ and Phlox paniculata ‘Laura’ – from an assortment of common landscaping plants were arranged in six replicate plots with 5 plants each. In 2021, according to phenological protocols, 862 observations were made and recorded with a remote multispectral and RGB plant photography method. 1262 RGB and 115 multispectral photos were taken of the studied plants. The collected data of the sequences and photos of the phenological phases were entered into the research i-garden database on the stationary server. In replicated plots, seven species were compiled. From phenological observation, it was found that there are significant differences between the testing sample plots in the growth process for Aster, Hosta, and Heuchera. A gradient of plant height and phenological phase starting time between plots has been observed. Nine pathogens were diagnosed through PCR tests and manual observation, of whom one was determined to be a virus, digitized in the i-garden database. This work allows using PCR primers to identify timely identification of pathogen infection and make the selection of multispectral digital imaging for automatic use in plant health diagnostics.