Chandra Krintz

Addressing the critical need to feed a growing global population without expanding arable land, researchers have developed a hybrid "lights-out" appliance that converges IoT, robotics, and machine learning to optimize California's $50 billion agricultural sector. This self-managing, fault-tolerant system leverages on-farm computation and cloud analytics to process multispectral imaging and sensor data for precise irrigation, fertilization, and disease detection while ensuring farmers retain full data sovereignty. Validated through partnerships with Fresno State and industry leaders, the architecture combines high-performance hardware with open-source software to deliver scalable, accessible tools for growers who lack technical expertise.