Heart of the Data Science Revolution… Mankato, MN?

@machinelearnbot

My job has the great advantage of bringing me in touch with some outstanding and truly brilliant people. As an example, last year I spoke at the PrecisionAg Vision Conference in Phoenix (and I'll be speaking there again this fall on October 12), and ran into a number of companies that were applying data science and big data principals to the world of agriculture. Having grown up in Charles City, Iowa, I have a special place in my heart for farming and the importance of agriculture to the health and financial success of our country. One company that I ran into – Aglytix – really impressed me with their application of data science to some of the fundamental decisions that farmers need to make in order to optimize yield while minimizing costs. Aglytix's approach to first identify the farmer's most important decisions and then apply data science to optimize those decisions plays right to the heart of the approach that we teach our customers (see Figure 1).


Glossary: Smart farming

ZDNet

This article was originally published on TechRepublic. Aerial imagery: Photos taken from the air, often with UAVs in smart farming. Used to assist farmers to determine the condition of a field. It is the integrated internal and external networking of farming operations as a result of the emergence of smart technology in agriculture. Agro-chemicals: Chemicals used in agriculture, which include fertilizers, herbicides, and pesticides.


AI for Good: How advanced crop intelligence can help solve food production challenges

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Farmers spend nearly half of their operational budgets on agrochemicals such as herbicides and pesticides. Unfortunately, they usually apply these to entire fields at a time, which generates high chemical costs and decreases the efficacy of the chemicals. Such widespread application of chemicals harms the environment, endangers human health, and increases the likelihood of chem ical-resistance in weeds, pests, and diseases. And, even with that damaging widespread application, loss to weeds, pests and diseases can range from 20-50%. But manually scouting and sampling to determine the locations of these problems is time-consuming and costly, and cannot easily account for the enormous variety of factors that affect crops.


How artificial intelligence is making our world a better place

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Google, Facebook and others fill their pockets with billions using our data. But artificial intelligence can do much more – for example ending world hunger. During my studies in artificial intelligence I didn't learn that much about how powerful computers are. Most of all, I learned that many things humans do are less complex than we think they are. Here you can find our introductory text on artificial intelligence by the physicist and neuroscientist David Hofmann (German) Artificial Intelligence – even though the term triggers associations such as Do we want that robots take care of us in the future, wonders Dirk Walbrühl here (German) humanoid robots, world supremacy and apocalypse, very often it means »no more« than data analysis. This might sound less exciting, but it is a powerful tool indeed – not only to understand what we click and buy, but also to find answers to questions that might improve the lives of many people.


How Machine Learning and Big Data Are Driving Progress in Indoor Agriculture - AgFunderNews

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Editor's Note: Malika Cantor is a partner at Comet Labs, a venture capital firm and research lab focused on artificial intelligence, and Micki Seibel is head of product at Orange Silicon Valley, part of French telecommunications giant Orange. The two organizations recently partnered on the publication of a report entitled: Bringing Digital Intelligence to Indoor Farming -- urban agriculture in the age of AI. Here Cantor and Seibel write about some of the report's key takeaways and data points. Ability to move production closer to the point of consumption Opportunity to drop genetic traits focused on outdoors -- pest resistance, drought tolerance, etc. -- in favor of traits for nutrient density and flavor. Higher nutrient density and less food spoilage due to shorter distance traveled Opportunity to broaden the crop portfolio as economies of scale are reached with current crops -(mostly leafy greens, cannabis, and vine crops like tomatoes).