This analysis demonstrates how autonomous UV-C treatments improve dollar spot management in turfgrass, suggesting enhanced monitoring through hyperspectral methods.
Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h−1, requiring 1.3 machines to treat 1 ha day−1, and a primary energy consumption of 55.06 kWh ha−1 for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions.
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Pippi et al. (2025) studied this question.
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