1. Real-time Monitoring and Mapping of Nitrogen Fusion of Data Science and Intelligent Sensor System Mingxuan Sun (Assistant Professor, Computer Science, LSU) Manas Ranjan Gartia (Assistant Professor, Mechanical Engineering, LSU), Magdi Selim (Professor, Agriculture Center, LSU)

2. Motivation

3. Enhances crop use efficiency Minimize losses in water to improve water quality A. S. Heinrich, R., "Nutrient uptake by spinach, UCCE Monterey County " (2013).

4. Chromatography Lab System vs Lab-on-a-chip X. Wang, M. R. Gartia et.al., "Audio jack based miniaturized mobile phone electrochemical sensing platform,” Sensors and Actuators B: Chemical 209, 677-685 (2015) Dionex Operation: require expertise Cost: high Operation: general public like farmers, tourist Cost: low

5. Real-time Mobile Sensor User Interface X. Wang, M. R. Gartia et.al., "Audio jack based miniaturized mobile phone electrochemical sensing platform,” Sensors and Actuators B: Chemical 209, 677-685 (2015)

6. Precise Nitrate Measuring using Mobosens X. Wang, M. R. Gartia et.al., "Audio jack based miniaturized mobile phone electrochemical sensing platform,” Sensors and Actuators B: Chemical 209, 677-685 (2015)

7. Nitrate Sensor Network M. R. Gartia et.al., "The microelectronic wireless nitrate sensor network for environmental water monitoring," Journal of Environmental Monitoring 14, 3068-3075 (2012).

8. Data Collection and Management

9. Predictive Analytics

10.  Data: Features -> Responses  Features: temperature, rainfall, soil types, crop types, fertilization rate  Response: yield  Modeling: machine learning tools (linear regression, deep learning) Challenges  Data challenges: spatial-temporal, noisy, missing data  Modeling challenges: latent features, multi-task learning Example: yield optimization

11. Distributed Learning Framework

12. Thanks! msun@csc.lsu.edu mgartia@lsu.edu MSelim@agcenter.lsu.edu