International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Mr.K.Srikanth, Dandu Amulya, Tanguturi Kameswari Smruthi, Gonuguntla Triveni,, Myneni DattaSri
Abstract: Water shortages combined with the rising demand for more food production have put pressure on the need for intelligent irrigation management. To address these challenges, this study devised a machine learning–based solution to soil moisture prediction that would enable smart irrigation practices to conserve water and increase crop yield. In order to derive accurate moisture content, the proposed model gathers environmental parameters such as temperature, humidity, rainfall, air pressure, and soil nutrients and then executes one linear regression algorithm. A Django web interface is embedded with the module through which farmers can feed in environmental data and get irrigation advice immediately. The test outcomes confirm that the application attains very high prediction accuracy and can save enormously on water. Thus, by integrating machine learning with web-based automation, a feasible, scalable, and resource-friendly solution is available for agriculture equipped with modern technology, especially for those places that suffer from water shortage.
