Applications of Machine Learning For Precision Agriculture and Smart Farming


  • Sai Gurrapu Virginia Tech
  • Nazmul Sikder Virginia Tech
  • Pei Wang Virginia Tech
  • Nitish Gorentala Virginia Tech
  • Madison Williams University of Mary Washington
  • Feras A. Batarseh Virginia Tech



Precision Agriculture, AI, Smart Farming


Recent deglobalization movements have had a transformative
impact and an increase in uncertainty on many
industries. The advent of technology, Big Data, and Machine
Learning (ML) further accelerated this disposition.
Many quantitative metrics that measure the global
economy’s equilibrium have strong and interdependent
relationships with the agricultural supply chain and international
trade flows. Our research employs econometrics
using ML techniques to determine relationships
between commonplace financial indices (such as
the DowJones), and the production, consumption, and
pricing of global agricultural commodities. Producers
and farmers can use this data to make their production
more effective while precisely following global demand.
In order to make production more efficient, producers
can implement smart farming and precision agriculture
methods using the processes proposed. It enables
them to have a farm management system that provides
real-time data to observe, measure, and respond
to variability in crops. Drones and robots can be used
for precise crop maintenance that optimize yield returns
while minimizing resource expenditure. We develop
ML models which can be used in combination
with the smart farm data to accurately predict the economic
variables relevant to the farm. To ensure the accuracy
of the insights generated by the models, ML assurance
is deployed to evaluate algorithmic trust.




How to Cite

Gurrapu, S., Sikder, N., Wang, P., Gorentala, N., Williams, M., & Batarseh, F. A. (2021). Applications of Machine Learning For Precision Agriculture and Smart Farming. The International FLAIRS Conference Proceedings, 34.