Machine learning and computer vision for plant health

机器学习和计算机视觉促进植物健康

• 批准号: 517528-2017
• 负责人: Hamarneh, Ghassan
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640425
• 关键词: Machine; learning; computer; vision; plant

Greenhouse plants must be continuously monitored for detecting pests and diseases. It is also important toexamine the crop's fruit production by estimating their numbers and the right time for harvesting. Theseprocesses are currently performed manually via tedious and time consuming visual inspection procedures,which can take weeks for an average size greenhouse. The main focus of this project is on developingcomputational tools for the automated, fast, and accurate interpretation of image data gathered from the plants.In particular, in collaboration with Ecoation, we will develop computer vision and machine learning softwaretools to analyze plant image data and identify important horticultural features and plant health status. Thisproject is essential for the company's development of machine learning and computer vision products tosignificantly reduce the need for human scouting for plant problems due to its precise and timely alertingcapabilities. With the development this project can bring, Ecoation anticipates incremental reductions in croploss due to the use of the Ecoation System in greenhouses. As the result of our early stage detection of cropstress, just in BC, it can save growers $20 million each year.

必须连续监测温室植物以检测害虫和疾病。通过估计其数量和合适的收获时间来检查农作物的水果产量也很重要。目前，这些杂志是通过乏味且耗时的视觉检查程序手动执行的，这可能需要数周的平均温室。该项目的主要重点是用于自动化，快速和准确的对从植物收集的图像数据的解释。特别是，我们将与Ecoation合作，我们将开发计算机视觉和机器学习软件工具，以分析植物图像数据并确定重要的园艺特征和植物健康状况。该项目对于公司开发机器学习和计算机视觉产品的开发至关重要，这严重地减少了人类搜寻植物问题的需求，因为它的精确且及时的警报能力。随着该项目可以带来的开发，由于在温室中使用了生态化系统，因此，生态化预计Croploss会增加。由于我们对农作物的早期发现，就在卑诗省，它可以为种植者节省2000万美元。