The Role of Supervised Climate Data Models and Dairy IoT Edge Devices in Democratizing Artificial Intelligence to Small Scale Dairy Farmers Worldwide

Publication Date


Document Type

Conference Proceeding

Publication Title

Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2


Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi



First Page


Last Page



Climate change is impacting milk production worldwide. For instance, increased heat stress in cows is causing average-sized dairy farms losing thousands of milk gallons each year; drastic climate change, especially in developing countries, pushing small farmers, farmers with less than 10–25 cattle, below the poverty line and is triggering suicides due to economic stress and social stigma. It is profoundly clear that current dairy agriculture practices are falling short to counter the impacts of climate change. What we need are innovative and intelligent dairy farming techniques that employ best of traditional practices with data-infused insights to counter negative effects of climate change. We strongly believe that “climate” is a data problem and the democratization of artificial intelligence-based dairy IoT devices to farmers is not only empowers farmers to understand the patterns and signatures of climate change but also provides the ability to forecast the impending climate change adverse events and recommends data-driven insights to counter the negative effects of climate change. With the availability of new data tools, farmers can not only improve their standard of life but, importantly, conquer perennial “climate change-related suicide” issue. It’s our staunch believe that the gold standard for the success of the democratization of artificial intelligence is no farmer life loss due to negative effects of climate change. In this paper, we propose an innovative machine learning edge approach that considers the impact of climate change and develops artificial intelligent (AI) models that is validated globally but enables localized solution to thwart impacts of climate change. The paper presents prototyping dairy IoT sensor solution design as well as its application and certain experimental results.


Climate change, Climate change-related suicides, Climate models, Dairy IoT sensor, Decision tree, Edge analytics, Embedded device, Farmer suicides, Hanumayamma dairy IoT sensor, Humidity sensors, Internet of things (IoT), Machine learning, Regression analysis


Computer Engineering