Bread wheat (Triticum aestivum L.) is the most widely cultivated broadacre crop in Australia. Common root rot induced by the soil-borne pathogen Bipolaris sorokiniana causes $30 million average annual loss to bread wheat production, yet is difficult to detect early in infection. This project aims to develop automated common root rot detection and quantification using multispectral and hyperspectral sensing, and machine learning algorithms, in glasshouse and field experiments. Canopy-level sensing using unmanned aerial vehicle and handheld sensors is expected to enable monitoring of subtle and non-visual foliage variations for early disease detection. The developed technology is expected to assist farmers in achieving whole-of-field disease scouting, potentially enabling reduced time and costs for soil-borne disease management while increasing wheat production.
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