The system leverages both field and synthetic data to optimize irrigation, predict runoff, and maintain turfgrass quality, making it a valuable tool for sustainable water management in agricultural and urban settings.
In this paper, we propose Weather-aware Runoff Prevention Irrigation Control (WaRPIC), a low-cost, practical solution that optimally applies water, while ...
The gathered data is used to build site-specific machine learning models that can accurately predict the Maximum Allowable Runtime (MAR) for each sprinkler zone.
WaRPIC involves homeowner-assisted data collection on the landscape. The gathered data is used to build site-specific machine learning models that can ...
DRLIC uses a neural network (DRL control agent) to learn an optimal control policy that takes both current soil moisture measurement and future soil ...
Aug 21, 2024 · Techniques like Extreme Learning Machine and k-Nearest Neighbors fill in missing soil data, ensuring more precise water management decisions.
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Apr 1, 2024 · ML algorithms can predict irrigation water requirements based on the study of evaporation processes through collected data and determine soil ...
Jul 10, 2024 · This dual-focus model underscores the benefits of integrating multiple algorithms for comprehensive plant health monitoring and irrigation.
To improve irrigation efficiency, this paper presents DRLIC, a deep reinforcement learning. (DRL)-based irrigation system. DRLIC uses a neural network (called.
In this paper a machine learning approach is proposed to provide required water for the rice crop. The precipitation level has been predicted using weather ...