This story was first published in the Davis College NewsCenter. See the original article here.
Using a combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) – think highly specialized remote sensing systems – a multi-institutional research team based at three Texas universities aims to establish an artificial intelligence-driven precision agriculture program that leverages UAV technology to address critical challenges in the Southern Great Plains region.
“These challenges include declining irrigation water, soil health deterioration, wind erosion, and maintaining profitability in one of the most intensively farmed regions globally,” said Wenxuan Guo, lead investigator and an Associate Professor of Crop Ecophysiology & Precision Agriculture at Texas Tech University and Texas A&M AgriLife Research.
“By creating educational and research programs that provide hands-on learning opportunities for students, we're committed to bridging the knowledge gap and facilitating technology development and adoption,” he said.
The three-year study is supported by a $750,000 grant from the USDA's National Institute of Food & Agriculture (NIFA). The project is titled, “Capacity Building for AI-driven Research and Education on UAS Applications in Precision Agriculture.”
Precision agriculture, with its reliance on cutting-edge digital technologies, enables efficient quantification of spatial variability in soil properties, crop growth and crop yield, Guo said. UAV technology with its capability to acquire data at high spatial and temporal resolutions is poised to transform agriculture by offering innovative solutions for water stress detection, plant growth monitoring, weed mapping, nutrient status detection, and precision fertilizer application.
Another aspect of the project relies heavily on harnessing the power of artificial intelligence - particularly machine learning - to analyze the vast amounts of data generated by UAS and other precision agriculture technologies.
“The goal is to provide valuable insights for decision support in crop management, thereby enhancing food security and sustainable agricultural production.” Guo said.
Along with Guo, the research team includes Associate Professor of Agricultural & Applied Economics Chenggang Wang, and Associate Professor of Computer Science and Director of the university's Data Analytics Laboratory Victor Sheng.
Also supporting the project are West Texas A&M University Associate Professor of Crop Physiology and Director of the Semi-Arid Agricultural Systems Institute Craig Bednarz, and Texas A&M University Professor of Crop Physiology & Agroecology Nithya Rajan.