By efficiently collecting large amounts of data, drones help guide advances in peanut breeding.
A team of faculty in the Virginia Tech College of Agriculture and Life Sciences is part of an award-winning, multistate project that has helped accelerate the use of unmanned aircraft systems in agriculture and natural resources.
The researchers were awarded the 2022 Excellence in Multistate Research Award for the ongoing project, “Research and Extension for Unmanned Aircraft Systems in U.S. Agriculture and Natural Resources.”
The award-winning project evaluates and identifies the most reliable, cost-effective and user-friendly drone platforms and sensors for monitoring and managing stressors in agriculture and natural resources and has helped accelerate the use of drones in ag systems.
Maria Balota, professor in the School of Plant and Environmental Sciences at the Tidewater Agricultural Research and Extension Center, is also principle leader of the multi-state Peanut Variety and Quality Evaluation project and Virginia peanut specialist, and is project chair of this award-winning research team. Also participating from Virginia Tech are Daniel Fuka, a postdoctoral associate in the Department of Biological Systems Engineering; Cully Hession, a professor and graduate program director in the Department of Biological Systems Engineering; and Joseph Oakes, the superintendent of the Eastern Virginia Agricultural Research and Extension Center.
“The contributions of this group to the multi-state effort were on large-scale water quality monitoring and high-throughput phenotyping of various crops and varieties by drone imaging,” says Balota. “Our research and outreach have helped overcome barriers and accelerate broader use of drones.”
Virginia Tech is the leading institution for the first year of the renewed project, which runs from September 2022 to October 2023.
Reducing Time And Costs Of Peanut Breeding
Cultivars with improved yield, disease resistance and environmental suitability are developed after years of extensive field selection. Identifying plant morphological and physiological characteristics is part of the process. For example, peanut plant height plays a role in soilborne disease infections because taller canopies are in less contact with the soil and remain dryer. Finding these favorable characteristics through traditional means is costly and time consuming. However, with the use of drones, researchers are learning to perform some of these assessments remotely, making them more time- and cost-effective.
Sayantan Sarkar, a doctoral candidate in Balota’s lab, developed a method to accurately estimate the characteristics peanut breeders are looking for. During two flight missions from the beginning of flowering to early pod growth, Sarkar measured a total of 104 peanut genotypes for a total of 1,248 data points for model development. A separate flight mission was used for model validation with accuracy ranging from 78% to 91%, depending on the model. The paper was published in the Plant Phenome Journal.
The ability to collect a significant amount of data in this manner is possible because of the collaborative research efforts and the development of hardware, software and detailed protocols for calibrating and using drones.
Balota says, “By efficiently collecting large amounts of data, drones can help guide better decision making, greater advances in plant and animal breeding and more profitable and sustainable management.” PG