A Patent-Oriented Architecture for Autonomous Irrigation Based on Plant Drought Stress Sensing and Wi-Fi-Enabled Decision Control - Habeeb, Akmam Ali (American Journal of Bioscience and Clinical Integrity, 2025)
As climate change continues to intensify and impact future threat scenarios and climatic conditions, the efficiency and sustainability of agriculture will rely on improved climate adaptability and mitigation innovation, responses from irrigation scheduling, and the integration of water scarcity and uncertainties in climate and water availability. Most existing commercial smart irrigation systems utilize only single-variable soil moisture thresholds, which do not sufficiently capture the actual conditions of real-time plant water status. This research presents a patent-oriented architecture for autonomous irrigation scheduling based on detecting plant drought stress and Wi-Fi-enabled control, designed for real-time, closed-loop adaptive systems in genuine field conditions. The customized architecture amalgamates the spatial arrangement of the sensor's deployment via distributed sensor nodes, where leaf surface temperature and optical soil moisture, soil moisture, and soil temperature of each crop are measured. Locally to each sensor, a composite Drought Stress Index (DSI) is formed from the water-stressed plant suppressive threshold. Sensor nodes periodically send DSI values to a central gateway using a low-power Wi-Fi communication approach. The gateway employs an adaptive decision algorithm that autonomously makes real-time control irrigation start, duration, and zone prioritization decisions based on managed DSI historical trends and plant response measured in DSI. Automated valve and pump control complete irrigation flow management, while all control operations are fully absent of human activity. Field validation was done using a controlled experimental design involving three groups, manual irrigation, standard soil-moisture based irrigation, and DSI based systems. The assessment was based on total water use, temporal stress pattern, plant growth metrics, and final yield. The design was able to lower water use by 35-40% relative to manual irrigation, and by 18-22% relative to conventional smart irrigation, while keeping yield metrics the same or better. One- way ANOVA with P<0.05 showed that water use efficiency and stress stabilization improvements were significant. Also, abnormal DSI response patterns were able to automatically flag irrigation fault detections, flow blockage, and pump malfunction. These findings indicate that the design is a scalable, energy efficient, and patentable solution to intelligent precision irrigation and sustainable agricultural water management.