Internet of Things and Machine Learning Based Intelligent Irrigation System for Agriculture

Abstract

A large level of interest has been shown in precision farming recently due to the growing demand for water and food worldwide. Producers will thus need there must be adequate water and agriculturally appropriate land to meet this need. Because with the limitations both materials are readily accessible, thus farmers need a strategy that modifies their behaviour. The secret to efficient irrigation is finding a way to provide a greater, better, and more profitable output while using less resources. There are many machine learning based Irrigation methods have been suggested to effectively utilize more water. Unusual weather conditions are not suitable for these algorithms since they have a limited learning ability. This innovation, which integrates intelligence, keeps performing better for longer periods of time despite the weather in any place. DLiSA forecasts the overall soil moisture levels for the next day, the duration of the irrigated, and the geographical extent of the water required to irrigate the field using a lengthy short attention span network. The simulation outcomes demonstrate that DLiSA makes better use of water over cutting-edge technology. prototypes used for research agriculture in the area.