Farming has been a traditional, manual and labor-intensive industry, and it
will continue to be so in the future. Agriculture is one of humanity's oldest businesses
and practices. Extrinsic elements, comprising climatic parameters and general
environmental variables, have strongly dictated crop yield and productivity. Disruptive
technologies such as the Internet of Things (IoT), Big Data Insights, Artificial
Intelligence (AI), Machine Learning (ML), etc., have significantly affected most every
enumerated industry area. Farming based on the implementation of the above
advancements leads to “Smart Farming”, also known as the “Green Revolution 4.0” in
agriculture, which combines agricultural methodologies with technologies to
accomplish desired processing efficiencies at manageable costs. The entire
development is software-based and sensor-monitored from farm to hand-held devices,
lowering overall costs, and increasing aggregate yield and ubiquity level, thereby
enhancing user engagement. Predictive analytics for crops may certainly contribute to
data-driven decision-making with the help of “failure prediction systems.” Climate
conditions can be tracked and forecasted to help with forecasting, from seeding to
development and delivery of the final crop to the consumer. An increase in global
concerns about food safety, as a result of large-scale flood disasters, puts more pressure
on smart farming methodologies. The study discusses more of the latest avenues of
research and future trends in smart farming with case studies about Indian states. The
work also examines major disruptive technologies that govern agricultural phenomena
in the twenty-first century.
Keywords: Crop yield, Data-Driven, Decision Making, Disruptive Technologies, Failure Prediction Systems
