Precision Irrigation Scheduling using Real-Time Environmental Data

Water use is a sensitive factor in crop production, especially in areas where water is scarce; thus, good water management is very encouraging in the farming sector. The other important factor in efficient water use is the time and quantity of water to use, also known as irrigation scheduling. Nonethe-less, conventional approaches to watering frequently result in wastage of wa-ter or, conversely, an insufficient supply of water because the data accrued is not in real-time. To this end, this study aims to develop a precision irrigation scheduling framework that integrates IoT sensors and M-L models. Constant online control is implemented on irrigation scheduling by developing two models namely linear regression and decision tree models using environmental parameters such as moisture, temperature, and humidity. The data inputs are improved through sensor fusion techniques making further refinements to the decisions about irrigation. Simulation results show that these real-time, da-ta-driven approaches outperform traditional methods, improving water usage efficiency by 15% and crop yield by 11%. Of these, the decision-tree model ap-pears to be more versatile about changes in its environment. These findings support the premise that incorporating monitoring of the environment in re-al-time coupled with AI, can enhance water management practices in smart agriculture. More research could be done to investigate the other variables such as nutrient distribution, thus coming up with a package solution to manage crops