Author: Tom Gauthier, Founder/CEO of AgTechLogic

 

I read many articles about how environmentalists and legislators have passed programs in the hopes of improving the environment. This might ‘feel good’, but unfortunately, many of these projects have little input from actual landowners, farmers and almost no input from business leaders or financial experts. However, this does not need to be the case. Technology evolution and data can bring significant positive results to those that want a better planet but also understand the need for a positive business case.
Yes, you read that accurately. We can deliver both today. Precision Agriculture was coined many years ago prior to the actual results. The concept just made sense. If we had the technology to precisely prepare soil, planting, fertilizing, weed management, irrigation in collaboration with weather, labor, equipment and many other factors we’d be far more efficient. Thus, saving money, reducing risk and being able to bring more yield per acre than in the past. The challenge was the data and decision making to combine all this data into precise locations required computing power that did not exist.
Did you know that the first electric car was introduced in the 1880’s, but it took over 130 years to make it efficient and affordable. Basic Precision Agriculture only began in the 1990’s.
 Small companies, like AgTechLogic can now bring highly effective and affordable compute, storage and AI technology to solve real problems and deliver the financial and environmental results for the customer.
 The adoption of technology in agriculture is accelerating and we are seeing financial models becoming more accurate, results can be seen in real time and shared with the public to show positive environmental impact. The dream of one-day pleasing the environmentalist and the finance manager are here. 

AgTechLogic is a Service-Disabled Veteran Owned Small Business that develops precision agriculture technology that captures data and images with artificial intelligence and machine learning algorithms to improve existing spray systems.

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