Deep Learning Based Agriculture Traffic Prediction Using Gated Recursive Deep Neural Network Iot Environment
Year: 2025
Type: article
Abstract: IoT traffic data can be used to apply deep learning technology to agriculture. The application of deep learning algorithms in agriculture has improved, resource management, and decisionmaking with positive outcomes. Precision agriculture can assist regulate crop yields by applying nutrients only when necessary to enhance crop quality and lessen adverse environmental effects. This is made possible by IoT capabilities. Identifying redundant data traffic remains a major research challenge in the field of IoT-based agricultural automation, despite the fact that numerous solutions have been offered. Additionally, farmers do not receive the necessary information about water levels, soil conditions, etc.
Source: Sylwan
DOI: 10.59879/ty0kz
Authors: M. Sofiya, M. Arulmozhi
Institution: Anna University, Chennai
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4409477401