Abstract
Weeds occur in patches across landscapes and vary in species, density, time of emergence, and growth rate depending on location. Precision weed management accounts for natural and management-induced variation to optimize inputs (control methodology) to reduce weed presence and improve crop yields across an area. When Anita and I started research on precision weed management, there were few options on data collection and fewer methods to change techniques on-the-go. Today, there are multiple methods to collect and process information and use these data to determine appropriate actions. In some cases, maps are made and utilized to direct herbicide type and appropriate rate. If autonomous weeders are used, data imagery collected by on-board sensors immediately signal an actuator for control placement. No matter the method, understanding which weeds are present, location, density, and the appropriate control and timing are critical for success. In the future, agronomists will be expected to have knowledge, skills, and abilities that range from traditional weed science (weed species identification, herbicide and other control information, potential crop loss) to today’s less common competencies, such as understanding robotics and manipulating and managing big data sets.
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Clay, S.A., Dille, J.A. (2021). Precision Weed Management. In: Hamrita, T. (eds) Women in Precision Agriculture. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-49244-1_5
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