We aim to develop, characterize, and test in real-use conditions, novel inexpensive wireless sensors for accurately measuring agriculturally relevant soil signals such as nitrate, ammonium, phosphate, potassium, and moisture. In addition, AI-enabled algorithms will be developed to predict the spatiotemporal distribution of these signals to support 4R Nutrient Stewardship (Right fertilizer source; Right rate; Right time; Right place) practices. The novel soil sensors will be developed at The University of California, Berkeley (UCB) using printing technologies and conventional electronics. The sensors’ performance will be evaluated at The University of California, Davis (UCD), under controlled laboratory conditions and in instrumented commercial almond orchard and processing tomato fields. In addition, supervised learning AI algorithms for spatiotemporal prediction of soil analytes will be developed at UCD. With rigorous testing and evaluation methods, the team will deliver affordable instrumentation for real time soil monitoring and AI-enabled algorithms that will enhance production, profitability, and environmental stewardship.