The use of advanced technologies for integrated management of root-knot nematodes (Meloidogyne spp., Nematoda: Meloidogynidae) : doctoral dissertation

Tropical root-knot nematodes (RKN) are extremely polyphagous pests causing large yield losses in agriculture. Some species, such as Meloidogyne ethiopica, M. inornata and M. luci (MEG) are highly related and difficult to distinguish. We sequenced 7 RKN isolates from the MEG group and following the genome assembly, determined their phylogenetic position within the genus. The genome assembly of M. luci SI-Smartno V13 is currently the most complete publicly available RKN genome, having 327 contigs, N50 contig size of 1.7 Mb, and final assembly size of 209.2 Mb. Phylogenetic analysis showed the positioning of all MEG-group isolates within a single monophyletic clade, and the species M. luci differed significantly from the species M. ethiopica and M. inornata. We tested the applicability of hyperspectral imaging for the early differentiation of biotic stress (RKN infestation) from abiotic stress (drought) in tomato plants. Using hyperspectral image analysis in the 400-2500 nm spectral range, it was possible to distinguish well-watered from water deficient plants with 100 % accuracy; and nematode-infested from healthy plants with 90-100 % accuracy. We also evaluated nematicidal activity and analysed the genomes of Bacillus firmus I-1582 and Bacillus sp. ZZV12-4809 and found multiple putative virulence factors. In the pot experiments as well as in the microplots, the strain I-1582 reduced M. luci infestation rates by 51-53 % compared to untreated control. I-1582 showed nematicidal and plant-growth promoting effects, as indicated by plant morphology measurements, relative chlorophyll content, leaf nutrient composition, and hyperspectral image analysis. Utilising supervised classification for hyperspectral image analysis, we successfully discriminated between B. firmus-treated and untreated plants – in the pot experiment we achieved 97.4 % and in the microplot experiment 96.3 % classification success