Abstract
The conventional management approach in agricultural production is applying water and chemicals at a fixed rate throughout the whole field. This practice did not consider the existence of in-field variability in spatial and temporal domains and results in over- and under-applications on crop and serious environment issues such as nitrogen leaking to and contamination of groundwater. Variable-rate technology is the key technology in agricultural production to address the in-field variability, maximize yield and profit, and minimize the agricultural inputs or footprints on the environment. Hence, this project pulls a multidisciplinary effort to address challenges in today's variable-rate technology (VRT) in agricultural production by tightly integrating sensing, networking, AI-based and process-based data analytics, and control systems with classic plant and soil biophysical principles and well-recognized management practices, to provide a generalizable and scalable framework for the real-time in-season variable-rate application. Meanwhile, it also improves the data analytics and decision-making models by turning the massive amount of data generated in daily agricultural production into a dynamic and distributed training process for model self-improving while keeping the farmers' privacy and computational efficiency.
Yeyin Shi
Dr. Yeyin Shi is currently an associate professor of agricultural intelligence in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln. Her research and teaching program aims to use advanced sensing and information technology as well as data analytics to automate and improve the decision making and application processes in agricultural production and natural resource management.
A major platform that Dr. Shi has been extensively working on is the unmanned aircraft systems (or drones). She and her group have developed data analytics with state-of-the-art computer vision and machine and deep learning techniques to monitor vegetation growth, biomass, and abiotic and biotic stresses, or predict yield. They also go beyond sensing but incorporate sensing and actuation for intelligent aerial applications. Dr. Shi’s research has been mainly funded by federal and state agencies.
Dr. Shi is also passionate about college education and has been part of the thrust in the development of precision and digital agriculture curriculums. She teaches Site-Specific Crop Management (Precision Agriculture), Technologies and Techniques in Digital Agriculture, and Aerial Imagery Processing and Analysis to undergraduate and graduate students.
Dr. Shi received her Ph.D. and M.S. degrees both from Oklahoma State University and worked as postdoctoral researchers at the Texas A&M University and the University of Florida prior to join in UNL.
Performance Period: 04/01/2021 - 05/31/2024
Institution: University of Nebraska-Lincoln
Sponsor: USDA
Award Number: 2039055