New biocontrol agents and automated tools to evaluate in vivo effect against Rhizoctonia solani

Introduction and aims of the study In recent years, interest has grown in the use of biocontrol agents as a viable alternative or support to synthetic chemicals for the management of phytopathogens and the enhancement of plant growth. In the present study, three bacterial strains have been tested as biocontrol agents against the fungal pathogen Rhizoctonia solani, a soilborne necrotrophic pathogen. Two of these strains (B04A33 Bacillus sp. and B04A42 Psychrobacillus sp.) were isolated from maize, while Lactiplantibacillus plantarum comes from olive. A Trichoderma-based commercial biocontrol product was used as control. Methods Rhizoctonia solani was inoculated into the soil two weeks before sowing. Treatments with Trichoderma and with bacterial strains were carried out by adding 10 mL of cell suspension (108 CFU/mL) to 100g of soil, at two points in time: one week before and on the same day of sowing. The crop responses to treatments were automatically evaluated by using a multiple rgb cameras setup installed in the greenhouse. The germination fraction detected with a custom image-analysis algorithm was compared to the manual count of the seedlings. After harvesting, several root-related parameters and the dry mass of 10 roots per replication were measured. Results and discussion S61 and B04A33 outperforming and equaling, respectively, the germination percentage observed in the control treatment with Trichoderma. The automated sensing system enabled to quantify seed germination in the different treatments with high correlation (R2 = 0,9437) with manual measurements. Root-related parameters shown to be related to roots dry mass. Finally, the root system of plants treated with bacteria was more developed than when infected with rhizoctonia (Fig.1). Conclusions Bacteria used in this experiment have a biocontrol effect against rhizoctonia and increase lettuce germination and growth. The use of cameras allowed to successfully acquire data regarding the germination and health conditions. These technologies could be helpful for automatization of lab evaluations and in-field assessments.