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: 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=636519
• 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.

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，收割机自动化是低灌木蓝莓产业的迫切需求。