SBIR Phase II: Visual Information eNvironment for Effective agricultural management and Sustainability

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be the development of software and monitoring system for the conservation of water resources and the economic sustainability of agricultural crop producers. Agricultural production uses a great deal of water. In California alone, 80% percent of all water usage goes to agriculture. As communities expand and drought conditions develop, the competition for scarce and expensive water resources has become intense. Growers need to minimize costs and water needs by making their irrigation practices more efficient. While new irrigation application technologies have reduced losses from wind drift, leakages and evaporation, large amounts of water are still be wasted as it washes through the soil past crop roots. The technology developed in this project will allow producers to track soil moisture movement through the soil profile and across fields. This will allow producers to develop irrigation strategies based on the unique conditions of their soil types and topography and reduce water waste. It will save producers money and enable them to stay in business and meet new governmental regulations regarding irrigation. Improvements in water management efficiencies and better crop management practices can increase crop production and uniformity, reduce food costs, and help preserve the environment.This SBIR Phase II project addresses priority challenges currently facing growers, producers, and agricultural scientists in effectively using water for crop (food) production. Soil and crop scientists have been examining the need for acquiring higher resolution moisture and temperature data throughout the root zone and across the landscape. A science-based approach is needed to optimally determine the number, location, and depths of sensors to accurately measure, and then model, soil properties for use in precise, effective and efficient water and crop management. Moreover, for perennial crops such as wine grapes, there also is a need to develop models and predictions relating these environmental conditions and historical data to crop quality and volume as well as long-term tree/vine health. The proposed technology uses novel visual analytics to organize the resulting massive data flows and novel predictive models to allow stakeholders to perform precision management of crops based on soil moisture conditions and other field conditions that have not been possible to date.