Texas Tech University

Jagadish aims at lowering flower drop in soybeans to enhance yields

Norman Martin

January 9, 2024

Texas Tech agriculture researchers are leading an effort to develop field-based imaging systems and innovative machine learning tools to address the challenge of lowering flower drops in soybeans. A 30-80 percent flower drop in soybeans grown across different regions in the U.S. has been an unresolved and persistent bottleneck that has limited soybean's ability to achieve full genetic yield potential.

“The tools we're developing have the potential to benefit U.S. and global soybean improvement programs aimed at lowering flower drop as a route to increase pod retention and yield,” said Krishna Jagadish, team leader and professor of crop-forage-livestock systems within Texas Tech's Department of Plant & Soil Science.

“I'm excited to continue working on this complex and interesting multi-regional and multi-institutional project, having Kansas State University, University of Missouri and University of Tennessee as collaborators with Texas Tech's Davis College of Agricultural Sciences & Natural Resources in the lead,” he said.

The on-going interdisciplinary project, titled, “Field phenotyping using machine learning tools integrated with genetic mapping to address heat and drought induced flower abortion in soybean,” is supported in 2024 by a $400,000 grant from regional soybean research collaborative programs, including the Atlantic Soybean Council, Mid-South Soybean Board, North Central Soybean Research Program, and Southern Soybean Research Program.

“The project utilizes cutting edge technology and scientific techniques and highlights a strategic partnership between the Whitacre College of Engineering and Davis College,” said Darren Hudson, Davis College Associate Dean for Strategic Initiatives & Assessment and the Larry Combest Endowed Chair for Agricultural Competitiveness.

“We want to foster close collaboration between these two colleges because it will take the expertise of both faculties to address some of the key grand challenges we face in agriculture and food, resources, and landscapes,” he said. “This is a very exciting continuation of an on-going project across multiple states attempting to solve the key problem of heat stress effects on soybean yields. Climate resilience of primary food and feed crops is essential to feed the world into the future.”

A key element of the project is that it has a heavy imaging-based – greenhouse and field – machine learning component through Professor Hamed Sari-Sarraf and his research team which is based within Texas Tech's Department of Electrical & Computer Engineering. “It's a good example demonstrating active collaboration between colleges to address a complex agricultural problem,” Jagadish said.

In addition to Jagadish and Sari-Sarraf, the transdisciplinary research team includes Post doctoral fellow Juliana Espindola, PhD Candidate Farshad Bolouri, Assistant Professor Gunvant Patil, Research Assistant Professor Impa Somayanda, and Chair and Professor of Crop Physiology Glen Ritchie.

According to the USDA Economic Research Service, the United States is the world's leading soybean producer and the second-leading exporter. Soybeans comprise about 90 percent of U.S. oilseed production, while other oilseeds—including peanuts, sunflower seed, canola, and flax—make up the remainder. In 2022, the top soybean producing states were Illinois, Iowa and Minnesota, accounting for more than 38 percent of total U.S. production.


This story was first published in the Davis College NewsCenter. See the original article here.