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

到2050年，农业将在养活全球预计超过90亿人口方面发挥至关重要的作用。然而，这一行业面临的令人震惊的情况是，世界上农场经营者的总数正在不断下降。在加拿大，这一比例在过去20年里下降了25%，主要原因是农业劳动力老龄化。矮灌木蓝莓是加拿大的主要园艺作物。在过去的十年里，加拿大蓝莓的总种植面积增加了61%。劳动力短缺也是低矮蓝莓产业不可避免的问题。目前，80%以上的低灌木蓝莓是由机械收割机收获的。机械收割机确实依靠操作员的技能和经验来实现更好的水果回收和质量，而对收割机的损害更小。然而，由于劳动力老龄化和低灌木蓝莓采收窗口极短(约3至4周)，低灌木蓝莓产业很难找到足够有经验的收割机操作员。因此，对低矮的蓝莓产业来说，收割机自动化是一个迫切的需求。以前使用的多传感器和数学优化方法不足以预测最优的收获方案，因为许多因素是相互关联的。人工神经网络(ANN)在农业领域有着广泛的应用，然而，以往的方法都不是在田间实时求解。这项研究提出了通过完成短期目标来实时优化最优低灌木收获；(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体系结构和方法，以及(Iii)利用人工神经网络/深度卷积神经网络进行实时建模。在此研究的基础上，利用神经网络建模程序和实时的田间传感数据，设计了一个可交付使用的嵌入式系统。神经网络/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技术的新时代。优化采收具有解决劳动力短缺危机的巨大潜力，因为它将用于收割机自动化，并将增加低灌木蓝莓产业的可持续性。采摘效率提高5%，每年将为加拿大低矮的蓝莓产业带来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，如使用人工神经网络/深度卷积神经网络进行动物行为分析。此外，该计划中训练有素的HPQ将在不同的农业部门工作，为加拿大高技能的农业自动化和机器人技术人员提供良好的基础。

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.********