Blog •  12/20/2018

Helping build a sustainable future with predictive agriculture

Written By Marion Limberger 
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Professionals and academics gathered on November 13 2018 in the university of Guelph, Ontario to discuss agriculture with less: sustainability from farm to fork. This symposium was part of the plant sciences symposium series, which aim at supporting the training of the next generation of scientists [1]. I had the chance to be invited as a speaker and learn a lot about sustainable agriculture.

2 Women and a Man standing at a Podium

The key speaker, Dr Martin Gooch, CEO of Value Chain Management International, highlighted that 49 per cent of the food waste takes place between food production and retail stores, while 51 per cent takes place at homes and restaurants [2]

Donut-Chart showing Food Waste

For my presentation, I showed how we can decrease the 10 per cent waste in the field using digital tools. I am part of the predictive ag department, where we leverage what is developed in research and development and see how we can develop digital prototypes to help our colleagues in the field. As an example, Corteva R&D recently developed a smartphone app that takes images of ears of corn in the field and instantly gives a kernel count and a yield estimate. With this tool, we know exactly how many bags of seed we can produce and sell from one single production field. This means we can better manage field inputs and have a positive impact on the environment.

A Hand holding an Ear of Corn 

The app uses machine learning, which is the same algorithm used by banking mobile apps to allow us to deposit checks through a smartphone app. Instead of deciphering handwriting on a check, this time the deep-learning algorithm identifies kernels on an ear of corn and helps estimate yield for an entire field.

This is but one of the digital tools used by Corteva that can help reduce the environmental impact of agriculture. A number of others are also being developed to help reduce food waste in transportation and packaging.



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