SBIR Phase II: Machine Learning Driven Synthetic Sensor for Plant Water Stress

This SBIR Phase II project will advance a novel system for sensing, predicting, and addressing plant stress, offering affordable guidance for permanent and specialty crop farmers. This guidance will simultaneously reduce irrigation water and agricultural chemical applications, while also increasing crop yields and farmer profits. The project will develop easy-to-use equipment and software that delivers guidance compatible with installed irrigation systems and processes. The addressable market for this technology is $3 B in the United States and $30 B internationally. The system's ability to reduce the use of irrigation water and chemicals has a profound societal benefit. The system will conserve scarce fresh water resources, allowing for population growth and an increase in irrigated land. The system will reduce water pollution from agricultural activities, addressing a $210 B annual problem in the United States; furthermore, it will improve agricultural yields, ensuring adequate food for growing populations, with reduced use of pesticides and fungicides. This SBIR Phase II project will demonstrate a software and distributed hardware system for measuring and predicting yield-reducing plant abiotic and biotic stressors, and delivering intervention guidance to farmers to mitigate these stressors. In order to predict plant stresses at a localized level, many data feeds must be fused and analyzed to create a synthetic sensor estimating plant water stress, predicting microclimatic conditions, and performing localized plant disease and pest modeling. The project will advance the fields of ultra-low power sensor arrays, wide-area networking, machine learning, and human interface design for big data interpretation. This project will expand a test system by adding microclimate and disease and pest model prediction algorithms for precision temporal and location-targeted interventions of irrigation, fertilizer, fungicide, pesticide, and biostimulant applications to mitigate plant stress. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.