Research | Badgujar Lab - Ag Automation

Our primary areas of research focus range from conventional agricultural machinery to mobile robots, which fall into three primary categories:

Develop and integrate a robotics-based solution: We believe that intelligently incorporated mobile robotics can provide a valid solution to our major challenges while deepening human-robot collaboration. Moreover, solar-powered, indefatigable, and multiple small robots (swarm robots) are more resilient to unfavorable weather conditions and would bring a paradigm shift in current agricultural practices. Therefore, we aim to mesh robotic technology with conventional and non-conventional food production.
Develop and integrate the precision agriculture technologies: Today’s cropping systems are characterized by intensive and uniform crop input application, which is environmentally unsustainable and often costly. Thus, we aim to develop and integrate precision agriculture technologies (equipped with advanced sensors, robots, and data) for input optimization and site-specific operation that is critical in reducing environmental footprints and costs.
Employ and harness the potential of artificial intelligence: Artificial intelligence has untapped capabilities to answer the agriculture system problems and make meaningful decisions from data. We aim to introduce and implement artificial intelligence in conventional agriculture machines and mobile robots.

Current Research Initiatives:

Fleet of robots for cropland expansion on highly sloped terrain field.
Design, fabrication, and testing of robotic grain drill feed mechanism.
Small ground vehicle testing and vehicle behavior modeling on slopes.
Artificial intelligence-based pest-infested soybean leaf image classification with deep learning techniques.
Fleet of robots to combat the invasive redcedar trees on native grasslands.
Automated insect species detection in a stored product environment.
Predictive artificial intelligence model in grain science and milling.