The Machine Learning Revolution in Agriculture

Michael Johnson Feb 7 2018The 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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