Field Notes

Why Field Work is Necessary

Fieldwork is defined as “practical work conducted by a researcher in the natural environment, rather than in a laboratory or office.”

When you hear the term “Field Work” what comes to mind? Getting out from behind a desk and interacting directly with your end customer, testing your product or service in a real-life scenario, or maybe even truly being in a field somewhere working with the land? Can it be dirty, expensive, and time consuming? Sure, but if done correctly what you learn and take away from those trials could just be the difference between success or failure for your company and product.

At Farmwave, we chose to tackle one of the most challenging problems facing agriculture today. To do this with clarity and efficiency, we believe that the best way to get feedback, gather data, create a product, and test that product is to be out in the field.

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Precision Image Processing and Recognition in Farmwave

Computer vision has quietly emerged as a major pillar in the foundation of AI. Image analysis has grown dramatically over the past few years and with greater access to the processing power of the Cloud. Object recognition is now astonishingly precise, and now with implementation of deep neural networks, error rates are less than 2%. 

When Dr. Fei Fei Li began constructing the ImageNet database, smart detection of objects was limited to yes / no options. (Yes, hot dog. No, not hot dog.)  It took almost 3 years for her team to assemble and organize over 3.2 million images for the initial data set of imagery. Eventually the set ballooned to 15 million images to organize the world’s objects into a language machines can understand. This paved the way for other datasets and refined the speed and efficiency with which machines could learn. Now instead of requiring thousands of images of an object, machines can be taught with a few hundred. 

Tesla’s Sr. Director of Artificial Intelligence Andrej Karpathy stated at a recent Tesla press conference that visual recognition is absolutely necessary for their push into autonomy. Each Tesla you see on the road today depends on deep neural networks interpreting HD footage shot by the 8 onboard cameras in real time. This enables the car to understand the environment it is self-driving in.  That technology of rapidly interpreting and understanding the world through visual data while in motion is now here. 

Farmwave has always used image analysis to empower its users. We were the first to leverage image processing algorithms to count the kernels on an ear of corn. This helped growers determine yield when factoring in stand counts. Soon we were tapping into cloud based systems to power our image recognition of diseases on crops. We call this detection tool our CORE (Cloud Optimized Recognition Engine).

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Communities & Enhancement Launch

Farmwave is excited to announce the launch of Communities and additional features that will enhance our users’ experience in the Farmwave web app.

Communication without intelligence is noise, intelligence without communication is irrelevant. - Alfred M. Gray

There is no question that in today’s world there is no shortage of tools for communication. We have found in agriculture that farmers, their teams, and their allies are forced to communicate across a dizzying array of different platforms. Our goal was to create a purpose-built space where farmers could have the conversations want and find the help they need- no matter where they were.

Our ability to provide a cohesive platform for growers from around the world to connect is what we believe one of the most important things for growers today.

Growers now have the ability to bring a crop consultant, an agronomist, into their field at the same time and get a better perspective on what might be happening, whether they’re in India or Indiana.

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