Reference number : GC-2017-Q1-02886, amendment number : , agreement type : G, recipient legal name : Chang, Young Ki (Dalhousie University), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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., program purpose french : 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., agreement title english : Real-time Optimization using ANN/Deep Convolutional Neural Network for Lowbush Blueberry Harvesting, agreement title french : Real-time Optimization using ANN/Deep Convolutional Neural Network for Lowbush Blueberry Harvesting, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada