Deep Learning Based Wheat Yield Prophecy and Irrigation Schedule Management to Reduce Water Waste
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Abstract
Water is an incredibly valuable resource on our earth; however, it could have threatened if not managed. The agriculture has the highest necessity for strategies to minimize water usage. Agriculture industry is implementing contemporary farming methods, and farmers are using cutting-edge digital innovations that are modernize decision-making and protability in agriculture. Numerous sectors have experienced the effective use of deep learning (DL) in the decision-making. There is impetus to use it in other significant fields like agriculture. Estimating yields is essential for managing crops, water planning, ensuring food safety, and determining how much work will be needed for the cultivation and storing of crops like wheat. Predicting wheat crop yield has the potential to diminish energy use like drop in water consumption. In this study, a deep reinforcement learning (DRL) model is implemented to forecast wheat crop yield by monitoring the environment via a DRL agent. Two bidirectional long short-term memory (BiLSTM) models are applied as the DRL agent for exploring the environment. One forecasts the water content in the land and other one was active to calculate the yield considering climate data, growth stage, growing degree days (GD), canopy cover (CC), standard evapotranspiration (ETo), irrigation level and water content in soil. The agent was trained to plan watering for a wheat crop, considering a place in Maharashtra, India. DRL agent provides a schedule identifying irrigation levels. The irrigation level is incorporated into the time required to water the area, facilitating the farmer to manage it more easily. The performance of the proposed model was compared to a xed base irrigation system. Water use decreased by 35% and wheat crop output increased by 5% when the trained model was compared to the fixed technique.
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