Real-time Optimization using ANN/Deep Convolutional Neural Network for Lowbush Blueberry Harvesting

使用 ANN/深度卷积神经网络实时优化低丛蓝莓采摘

• 批准号: RGPIN-2017-05815
• 负责人: Chang, YoungKi
• 金额: $ 1.46万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711824
• 关键词: Real; time; Optimization; using; ANN

The agricultural industry will play a vital role in feeding over 9 billion predicted population on the globe by 2050. However, the alarming situation facing this industry is the total number of farm operators in the world is constantly declining. In Canada, it declined by 25% in the last two decades mainly due to an aging agricultural labor force. Lowbush blueberry is a dominant horticultural crop in Canada. The total acreage of a blueberry in Canada was increased by 61% in the last decade. Labour shortage is also an inevitable problem of lowbush blueberry industry. Currently, more than 80% of lowbush blueberry is harvested by mechanical harvester. The mechanical harvester really relies on operator skills and experience for better fruit recovery and quality with less damage to the harvester. However, due to the aging labor force and an extremely short harvesting window for lowbush blueberry (around 3 to 4 weeks), the lowbush blueberry industry has a difficulty to find enough experienced harvester operators. Therefore, harvester automation is an urgent need for the lowbush blueberry industry. Previously used multiple-sensing and mathematical optimization is not enough to predict the optimal harvesting set-up because so many factors are interrelated. Artificial Neural Network (ANN) was widely used for many agricultural applications however, none of the previous approaches were real-time in field solutions. This research proposes a real-time optimization for optimal lowbush harvesting by accomplishing short term objectives; (i) to develop a sensor fusion system, (ii) to develop an architecture and methodology for a hardware based fast image processing system, and (iii) a real-time modeling utilizing an ANN/Deep Convolutional Neural Network. Based on this research, a deliverable embedded system will be made in the future deriving the optimum parameters using the neural network modeling program and real-time field sensing data. The real-time embedded system of ANN/ Deep Convolutional Neural Network is a new era of agricultural automation and robotics. The optimization of the harvesting has huge potential to solve labour shortage crisis as it will be utilized for harvester automation and will increase the sustainability of the lowbush blueberry industry. Five percent increase in harvesting efficiency would result in $55 million of revenue to Canadian lowbush blueberry industry per year. The optimization of the harvesting will serve a good foundation for the Bio-systems Automation Research Program as it can be easily transferred to other cropping systems and other agricultural sectors like animal behavior analysis using ANN/ Deep Convolutional Neural Network. Moreover well trained HPQs from this program will work within different agricultural sectors, providing a good foundation of highly skilled Canadian agricultural automation and robotics personnel.

到2050年，农业将在养活地球仪预计超过90亿人口方面发挥至关重要的作用。然而，该行业面临的令人震惊的情况是，世界上农场经营者的总数不断下降。在加拿大，它在过去二十年中下降了25%，主要是由于农业劳动力老龄化。矮丛蓝莓是加拿大的主要园艺作物。在过去的十年里，加拿大蓝莓的总面积增加了61%。劳动力短缺也是低丛蓝莓产业不可避免的问题。目前，80%以上的低丛蓝莓是通过机械收获机收获的。机械收割机真正依赖于操作员的技能和经验，以获得更好的水果回收和质量，同时减少对收割机的损坏。然而，由于劳动力老化和低丛蓝莓的收获窗口极短（约3至4周），低丛蓝莓产业很难找到足够有经验的收割机操作员。因此，收获机自动化是矮丛蓝莓产业的迫切需求。 以前使用的多传感器和数学优化是不够的，以预测最佳的收获设置，因为这么多的因素是相互关联的。人工神经网络（ANN）被广泛用于许多农业应用，然而，以前的方法都没有实时的现场解决方案。本研究提出了一种实时优化的最佳lowbush收获通过实现短期目标;（i）开发传感器融合系统，（ii）开发基于硬件的快速图像处理系统的架构和方法，以及（iii）利用ANN/深度卷积神经网络的实时建模。在此基础上，未来将制作一个可交付的嵌入式系统，使用神经网络建模程序和实时现场传感数据导出最佳参数。 ANN/深度卷积神经网络的实时嵌入式系统是农业自动化和机器人的新时代。收获的优化具有解决劳动力短缺危机的巨大潜力，因为它将用于收获机自动化，并将提高低丛蓝莓产业的可持续性。收获效率提高5%，每年将为加拿大低丛蓝莓产业带来5500万美元的收入。收获的优化将为生物系统自动化研究计划奠定良好的基础，因为它可以很容易地转移到其他种植系统和其他农业部门，如使用ANN/深度卷积神经网络进行动物行为分析。此外，来自该计划的训练有素的HPQ将在不同的农业部门工作，为高技能的加拿大农业自动化和机器人技术人员提供良好的基础。