Smart Irrigation with Machine Learning Based Decision Support System


  • Parul Agarwal, et al.


Citizens in metropolitan centers, like developed areas, possess access to all basic amenities, such as electricity with few power outages, food, comparatively good roads and building infrastructure, and so on. Nevertheless, this may not be the case in rural areas, where power outages, agricultural issues, and inadequate water processing and marketing for various purposes, among other things, affect the majority of villages. As an outcome of our study, there are currently a number of variables that could affect the progress of a smart village. The define objectives of this study is largely automated hydrated agriculture, that is accompanied by an appropriate rainfall forecast system that can assist us in determining which farms are best for development in a certain location.A new method is also introduced, wherein the plants must be rinsed properly and the amount of time it takes for the motor to turn on. We can also save water and electricity that would otherwise be wasted on watering crops and put it to better use for village residents by re-using it for other purposes.




How to Cite

et al., P. A. (2021). Smart Irrigation with Machine Learning Based Decision Support System. International Journal of Modern Agriculture, 10(2), 4003 - 4013. Retrieved from