Project Team
- Dr. Yi Lu, Professor, Dept. of Chemistry
- Dr. Jian Peng, Assistant Professor, Dept. of Computer Science
- Dr. Wendy H. Yang, Associate Professor, Dept. of Plant Biology and Geology
- Dr. Li-Qing Chen, Assistant Professor, Dept. of Plant Biology
Abstract
It is widely recognized that fertilizers and pesticides play essential roles in agriculture and yet most management of these compounds rely on technologies from the last century and cannot meet the demands of the modern farm. Specifically, while nutrients/pesticides can be measured using laboratory-based approaches, the methods are time-consuming, making the data unlikely to represent in situ conditions. Furthermore, the required analytical instrumentation is unaffordable and inaccessible to most farmers. While some automated sensors have been developed for aqueous media, they cannot be deployed in heterogeneous soil matrices, nor be adopted to new emerging targets. To meet these challenges, we have assembled a team of researchers with complementary expertise to develop novel low-cost, in-field sensors that are deployable throughout farm fields for on-site and real-time monitoring of nutrients and pesticides. We also plan to integrate the in-field sensors with machine learning by providing novel high spatiotemporal resolution datasets from the in-field sensors for data analyses and prediction. The innovation of this proposal is low-cost sensors that allow farmers to measure multiple targets, including new emerging targets, in ground water, soils and plants at unprecedentedly high spatiotemporal resolution. Furthermore, the combination of real-time, wireless sensors with machine learning will allow not only rapid data processing, but also significantly enhanced understanding of the pattern of the distributions and migrations of nutrients and pesticides in the groundwater, soil and plants and their correlation with the crop health and yields. This integrated in-field monitoring system will provide farmers with timely information to act on, using the sensor data and machine learning to inform when and where nutrients and pesticides should be applied. Once feasibility of our approach for smart precision agriculture is demonstrated through this seed grant, we plan to seek outside funding from federal agencies with potential collaborations from companies for in-field applications.