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A Working Title

Posted on February 09 2018 | Author: Molly Gallant

For those of you that read the title of this post and assumed I simply forgot to add in a title and instead left a placeholder there, you would be partially right (everyone knows - titles can be tricky!) but also mistaken. Ever since I began contemplating my career and more recently, taking my first job in the Agri-Business sector, I struggled with categorizing myself and my capabilities into a couple of words.

From a young age, we are asked what we want to be when we grow up. Lawyer, doctor, and teacher were all common answers. And to some extent, a desire to label myself in this simplistic manner has held true through to adulthood. With a background in nutritional sciences and several diverse interests, answering this question can be difficult if not panic-inducing. Upon several occasions, exasperated, I’ve wanted nothing more than to be able to reduce my interests and role to one word, one profession.

Participation in the New Graduate and Mentorship Program through Bioenterprise has offered me the opportunity to work in a role that I struggle to define. However, as I learn and grow, I am becoming increasingly comfortable with this uncertainty.

In writing this post, I was asked to first identify my role. Inevitably, this straightforward question has allowed me to evaluate and consider my work, my role, my employer’s expectations as well as my own goals. Sometimes, when you’re working for a small company, and when you’re as exceptionally lucky as I am, your position is something fluid and unique. I have been working with Henry’s Tempeh now for the past few months and sometimes feel no closer to defining my role as I was the first day I stepped into their shop. Henry’s Tempeh is a small food manufacturing company located in Kitchener, Ontario that produces high-quality, delicious tempeh. Using local, GMO-free and organic soybeans that are fermented, pasteurized and packaged, they produce an excellent plant-based protein alternative.

I was hired to assist with the monitoring of their HACCP food safety program. The more comfortable I became with these responsibilities, the more efficiently I was able to manage them. As I learned more about the company, I became involved with their marketing strategies, business goals and product development initiatives. I’ve even stepped into production on several occasions and just ran my first delivery last week. Interacting directly with the owners and being asked to help with problem-solving issues has contributed to both my personal and professional growth, increased confidence as well as provided an excellent learning experience.

I recall filling out a form for a conference at work one day and of course, being asked to state my position. I cautiously looked to my co-worker Jason for an answer. He simply shrugged, and suggested I make up a title.

I can’t even remember what I put down that day but ultimately, it’s not your position title that is important. More important is the work you do, the people you work with and your own enthusiasm and passion that create your role.

Author:
Molly Gallant
Analyst, Henry's Tempeh
Bioenterprise Recent Graduate & Mentorship Program






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The Machine Learning Revolution in Agriculture

Posted on February 07 2018 | Author: Michael Johnson

The use of machine learning in agriculture is growing and providing substantial benefits to the agriculture industry. Machine learning allows computers to adapt to new circumstances similar to a human. This provides the computer with the ability to ‘learn’. This has provided the agriculture industry with self-driving vehicles, systems to identify crop health problems, and weed detection.

Machine Learning Applications in Agriculture

Genetic Engineering: Machine learning has been used in agriculture to determine favourable traits for crops. Machine learning determines the traits needed for plants to survive in specific climates, weather, and soil. In addition, it can also be used to determine how to make plants resistant to insects and diseases, and seeds adaptable to unfavourable conditions. This is performed through deep learning, where decisions are made based on the analysis of data.

Weed Detection: Blue River Technology uses machine learning to identify weeds and apply herbicides. This is necessary to prevent the use of herbicides on areas without weeds. The technology also reduces the waste of herbicides.

Disease Detection: Machine learning is used to identify healthy and unhealthy potatoes through photo analysis. This decreases costs through reducing the use of labour in detecting unhealthy potatoes. Moreover, this technology has been used to identify the health of crops through analyzing satellite images of farmland. The machine learning systems can detect the health of crops based on photo-patterns. As well, they are using this technology to identify healthy and unhealthy cows based on photo analysis. This photo analysis can determine if a cow has a physical health problem.

Self-driving vehicles: Machine learning and computer vision are used to create self-driving farm equipment. Computer vision is used to determine hazards and guide the vehicle through the use of cameras.

Tesla has created self-driving cars that use machine learning and computer vision to drive long distances. Tesla states that “all Tesla vehicles produced in our factory, including Model 3, have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver” (Tesla, 2018). This technology is revolutionizing the transportation industry and will one day make all vehicles self-driving.

Irrigation: Machine learning has been used to perform efficient irrigation through closed loop systems. The machine learning system determines the quantity and start of irrigation based on generated data. This has decreased costs, reduced environmental damage, and decreased water waste.

External to Agriculture

Facebook: Facebook uses machine learning to send personalized advertisements to their users. Advertising is Facebook’s primary source of revenue. Facebook tracks the likes and dislikes of users in order to send personalized advertisements to users based on their preferences. This has improved advertising revenue through higher clicks and engagement.

IBM: IBM created Watson, an artificially intelligent robot that analyzes data. Watson has been used to forecast precipitation, humidity, temperature, and wind. As well, Watson has been used for medical treatment decision making.

Software: Using statistical software such as STATA, R, and Microsoft Excel can provide substantial benefits to businesses. These software products can be used to determine trends in data through regressions. These trends can be used for data-driven decision making in business. This software can create predictions through forecasting and machine learning. For example, R can be used to analyze photos for specific characteristics.

Sources:
BMVA
Tesla
Digital Journal
Precision Ag
Victor John Tan

Author:
Michael Johnson
Junior Analyst, Economics
Bioenterprise Recent Graduate & Mentorship Program






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