The Role of AI and Machine Learning in Advancing Sustainable Biologicals in Agriculture
AI and machine learning solutions can help identify new microbial strains with beneficial agricultural properties, marking them as good candidates to improve biologicals such as biopesticides, biofertilizers, and biostimulants.
2021 Top Article - Advancements in Precision Agricultural Insights
In five to 10 years we will see more aerial and ground robots operating alongside the farmers. And farmers will be able to proactively manage their farms using modern AI capabilities and generate better yields and profits-with optimum sustainability.
What Roles Will AI And Machine Learning Have In Feeding The World?
Models and data analytics not only recap what is already occurring between water and plants across expansive rows of corn, they can actually predict what will come in the hours, days and weeks ahead.
How Machine Learning Plus Weather Information Can Help Us Feed The World
Even if we had perfect weather data everywhere all the time, there are many more uncertainties in a lot of the weather-driven agronomic models that attempt to predict crop stage, disease pressure, and crop performance
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How to overcome GNSS limitations with RTK correction services
Although GNSS offers ubiquitous coverage worldwide, its accuracy can be hindered in some situations - signals can be attenuated by heavy vegetation, for example, or obstructed by tall buildings in dense urban canyons. This results in signals being received indirectly or via the multipath effect, leading to inaccuracy, or even blocked entirely. Unimpeded GNSS positioning in all real world scenarios is therefore unrealistic - creating a need for supporting technologies, such as real time kinematic (RTK) positioning and dead reckoning, to enable centimeter-accuracy for newer mass-market IoT devices.
