Abstract
Irrigation in agricultural lands plays a vivacious role in water and soil conservation. Future prediction of soil moisture content using real-time soil and environmental parameters may provide an efficient platform for agriculture land irrigation requirements. In this paper we have proposed one optimization technique like Gradient Descent with Momentum is used to train neural network pattern classification algorithm. The algorithm is tested for the prediction of soil moisture content in each one hour advance by considering eleven different soil and environmental parameters collected during a field test. The prediction errors are analysed using MSE (Mean Square Error), RMSE (Root Mean Square Error), and R-squared error. The performance of the proposed soil moisture content prediction is well documented in this article.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
