Post-Doc: Farming as a Service- stereo-spatio-temporal anomaly detection- BC-573Desired discipline(s): Engineering - other, Engineering, Computer science, Mathematical Sciences, Agriculture, Natural Sciences
Company: Ecoation Innovative Solutions Inc.
Project Length: Longer than 1 year
Preferred start date: 05/01/2021
Language requirement: English
No. of positions: 1
Preferred institutions: McGill University, Université de Montréal, University of Montreal, University of Toronto
About the company:
Ecoation is an agricultural clean tech company providing unique early-stage actionable intelligence about the health and quality of plants in greenhouses, yield assessment, crop registration, the quality of work completed by workers, and growing conditions (climate and light levels). Our objective is to assist growers with actionable intelligence and empower them to minimize prophylactic pesticide use, reduce their risk and cost, increase their revenue and offer a closed-loop growing service. We provide data driven grower assist technology consisting of our collaborative robotic platform with edge computing (OKO) and the supporting data handling and processing (our back-end) and presentation tools (front-end). In addition, we provide human consultation services through the platform. Using our technology, growers can prevent crop loss, increase greenhouse productivity, reduce cost, and increase plant resource input efficiency. This reduces GHG emissions, water consumption and waste. Our robots are equipped with patented sensing technology which continuously collects data on plant stress, microclimate, growing conditions and canopy structure and phenotype. Ecoation has been in the market commercial for more than2 years with robots in Canada, US, Mexico, various EU countries and soon Russia and Japan.
Please describe the project.:
Ecoation collects masive and diverse types of Data from every sqm of greenhouses with a mobile Co-Bot (OKO) and present it to the growers in form of time serries change and analysis. The goal of the company is to do diagnostic anomaly detection and run causal inference for example if an area has low production of the fruit, we need to find out what is the main reason among multiple variables that we monitor and how we can remedy this. Combined with an autonomous robot that fix the issues in the greenhouse without pesticides, this “find and fix” solution is going to enable us to offer what we call FaaS or Farming as a Service. This project is about sterio-spatio-temporal anomaly detection and causal inference with the goal of increasing production and reducing cost of farming.
We are looking for a Post Doctoral Fellow who can join out team of Data Scientists to bring expertise in deep learning, time-series analysis and ML-clustering methods such as DBSCAN and MSET.