Real-time plant discrimination and weed detection platform

实时植物辨别和杂草检测平台

• 批准号: LP140100774
• 负责人: Prof Kamal Alameh
• 金额: $ 19.03万
• 依托单位: Edith Cowan University
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-10-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP140100774
• 关键词: Real; time; plant; discrimination; weed

The aim of this project is to develop an advanced, robust, commercially ready, laser-based green-from-green plant discrimination and weed detection sensor, capable of identifying different plants and weeds in the field, and allowing precise herbicide spraying of targeted weeds all in the same pass. Through the incorporation of novel multi-band sensors and the development of adaptable, configurable lasers, this first-of-its-kind system intends to see a dramatic improvement over current approaches to weed targeting. The outcome of the project aims to achieve optimised weed control, resulting in increased crop yields, reduced environmental impacts, decreased risk of herbicide resistance and substantial cost-savings.

该项目的目的是开发一种先进的，强大的，商业化的，基于激光的绿色从绿色植物识别和杂草检测传感器，能够识别不同的植物和杂草在外地，并允许精确的除草剂喷洒目标杂草都在同一个通道。通过采用新型多波段传感器和开发适应性强、可配置的激光器，这一首个同类系统有望大大改善目前的除草方法。该项目的成果旨在实现优化的杂草控制，从而提高作物产量，减少环境影响，降低除草剂抗性风险并节省大量成本。