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Greenhouse gas (GHG) emissions from the agricultural sector have significant environmental impacts, including contributing to climate change and air pollution. Reducing emissions from the agricultural sector is a crucial step in addressing environmental and economic challenges. While several regulatory measures have been put in place, small and medium farms struggle to control their emissions due to a lack of simple, affordable, and user-friendly emission management tools.
Finding effective and scalable solutions that can be widely adopted by farmers and producers is a complex task that requires addressing a range of interconnected factors. In this project we present Preserve, a low-cost solution that relies on in-situ real-time measurements from farms and provides guidance through predictive models while allowing farmers to monitor measurements via a user-friendly cloud interface. Preserve uses predictive analytics and multiple regression models in Python to alert farmers and producers to take preventative measures when the model predicts an impending emissions spike.
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