Intelligent Irrigation Scheduling Through Local Weather and Soil Integration

Arjun Mehta

Intelligent Irrigation Scheduling Through Local Weather and Soil Integration

Author(s)	Arjun Mehta
Country	United States
Abstract	Efficient irrigation scheduling increasingly depends on the ability to reconcile two forms of information that are often used separately: atmospheric water demand and the actual water status of the crop root zone. Weather-based approaches estimate crop water requirements from meteorological variables, whereas soil-based approaches respond directly to changes in soil moisture. Each method has practical strengths, yet either method used independently can produce avoidable irrigation because short-term rainfall, evapotranspiration, soil water storage, sensor uncertainty, and crop-stage requirements interact dynamically. This study develops and evaluates an intelligent irrigation scheduling framework that integrates local weather observations with root-zone soil moisture information through a confidence-weighted decision algorithm. The framework combines reference evapotranspiration, crop coefficients, effective rainfall, soil water depletion, irrigation-system efficiency, soil-moisture thresholds, and a short-horizon rainfall veto mechanism. Because no field dataset was supplied for the present study, evaluation was conducted using an explicitly labeled synthetic eight-week crop-season scenario rather than presenting simulated observations as empirical field measurements. Four scheduling strategies were compared: fixed-interval irrigation, weather-based scheduling, soil-moisture-based scheduling, and the proposed integrated weather–soil method. The simulated integrated strategy applied 312 mm of irrigation compared with 420 mm under fixed scheduling, representing a 25.7% reduction in applied irrigation water. It also achieved the highest simulated root-zone moisture-target compliance at 91.1%, the lowest estimated deep-percolation loss at 12 mm, and an irrigation water-use-efficiency proxy of 2.56 kg m⁻³. The results indicate that local weather and soil integration can improve the timing as well as the quantity of irrigation by reducing unnecessary applications while maintaining favorable root-zone conditions. The proposed architecture is intentionally computationally lightweight and can therefore serve as a foundation for low-cost edge-based irrigation decision systems. Field validation across different crops, soils, climates, sensor configurations, and irrigation technologies remains necessary before operational performance claims can be made.
Keywords	: smart irrigation; irrigation scheduling; soil moisture; local weather; evapotranspiration; precision agriculture; water-use efficiency; Internet of Things; decision support; sustainable agriculture
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2021
Published On	2021-06-02

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Irrigation Scheduling Through Local Weather and Soil Integration

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