Machine learning algorithms plays a significant role in precise resource optimization like irrigation water and nutrient management. Based on the real time data, the decision making is made easy by predicting the need and subsequently supplying it the precise requirements. Irrigation scheduling could be done based on available wheather parameters, sensor based precised data on soil water and the crop water requirements for millets at a particular period. All these calculations can be automated using machines. Real time based moisture content of the plant and temperature of it can also be included in the algorithm as an input. By combining all this the actual amount needed can be calculated and applied accordingly which optimize the over or under use of water resources.

Role of Machine Learning in Millet Irrigation and Nutrient Management:

• Data-Driven Decisions: Analyzes soil, weather and crop data to optimize irrigation schedules and nutrient application.
• Predictive Modeling: Forecasts water and fertilizer needs based on growth stage and environmental conditions.
• Resource Efficiency: Minimizes waste, reduces costs and improves yield by delivering inputs precisely when and where needed.
• Stress Detection: Identifies early signs of drought or nutrient deficiency for timely intervention.

hybrid algorithms that combine machine learning and opnitization can support to find solutions in several areas, incluing agriculture.

In this paper there are several example of algorithm that uses ML and opt stategies and may helps your research

Article Hybrid approaches to optimization and machine learning metho...