Abstract:BackgroundThe seedling stage is the most vulnerable period of growth and development for annual weeds and an important target for weed management operations. To address this, several weed emergence models have been developed, but none are commercially available. Therefore, this study aims to develop a web application that implements predictive weed emergence models for eight different weed species, utilizing weather data sourced from public weather stations.ResultsLolium rigidum Gaudin presented a mean root me… Show more
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