Articles

Moonshots, Models, IoT and Machine Learning in Agriculture

Gayathri Gopalakrishnan

What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?   Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -  scientists in companies and academia worked on developing...

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Innovation In The Water Sector

Gayathri Gopalakrishnan

I was at Imagine H2O’s “Water Innovation Week” conference this week - virtually, of course! Imagine H2O is a wonderful resource and accelerator for startups in the water space, and their program this week was an excellent representation of water’s central role, not just in our daily lives, but also in the clean technology sector in general.   In most of the developed world, water isn’t really at the forefront of most people’s minds. Turn on the tap, you get clean, free flowing water - and unless you’re in the water sector, you’re probably not thinking about things like aging...

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Agtech, Farmtech, Foodtech, Livestock tech - the market for agriculture and data science over the last decade

Gayathri Gopalakrishnan

It’s always interesting to take a look at how trends and predictions about new technologies and their ramifications for different sectors pan out. And that’s no different when it come to data science and clean technology.   A graph that often comes up when discussing how new technologies develop is the “Gartner cycle of hype”. This is the idea that all new ideas, concepts and technologies invariably go through several stages in their development - they all start with excitement as the promise of new technology opens up possibilities that seem limitless, followed by a crash course in reality when...

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Building Models With Missing Data

Gayathri Gopalakrishnan

Have you ever worked with a real-world problem where you have all the data that you need in a form that you could easily use to build models?   In the case of most problems, we find that data are missing, or there are errors in how the data are measured, or we’re faced with different types of data that need to be integrated. That’s been especially true in many clean technology fields - water, energy, climate, sustainability, ecosystem restoration and agriculture among them.   So, how do we deal with data with so many challenges?   One way is...

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Coming this Sunday, September 20th: Bayesian networks in clean technology live, virtual workshop

Gayathri Gopalakrishnan

How do you find out why new technologies are being adopted? How do you find the early adopters and figure out why they are using these new technologies?   As startups and individuals build new tools and applications in agriculture, water, energy, sustainability, forestry and climate - some of the the biggest questions they face are understanding who is likely to adopt these technologies, the parameters governing these decisions and how they interact with each other.   So, how can this be measured and modeled quantitatively? Welcome to the wonderful world of Bayesian networks!   Bayesian networks are powerful machine...

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