UAV technology improves rice selection and breeding

Rice researchers at the A&M AgriLife Research Center in Texas, Beaumont, have taken the selection and breeding of rice varieties to the next level with an aircraft-based project (UAV). The team will use the UAV to take real-time snapshots of rice crops, remove crop phenotypic traits from those images, and analyze information to reveal fertile rice genotypes. press release from Texas A&M AgriLife.

Researchers hope to avoid one of the main barriers to data collection – a labor-intensive and time-consuming skilled workforce for manual field data collection. Yubin Yan, a senior biosystems analyst at the Beaumont Center, will lead the project, which will run a three-year grant of $ 650,000. USDA National Institute of Food and Agriculture (NIFA).

The main goals of the research:

  • Calculate the main phenotypic characteristics of rice growth and development.
  • Record UAV images of rice genotypes at important stages of rice growth.
  • Develop advanced image processing algorithms to eliminate key phenological, morphological, and architectural features.
  • Develop a digital rice selection system to discover the best-performing genotypes using data integration and decision-making on multiple traits.

The new wave of UAV technology

“Traditional manual measurement of the phenotypic properties of rice takes a very, very long time,” Yang shared. “Hiring skilled and experienced workers is becoming increasingly difficult. UAV technology and advanced image processing could be a cost-effective and reliable alternative. We can use UAVs to capture images of rice at key growth stages and develop algorithms to extract different phenotypic traits for hundreds or even thousands of rice genotypes.

There will be several UAV flights throughout the rice harvest season to capture thousands of UAV images and basic truth information. Different angles of the chamber will be used to help analyze the location of the stands as well as the gaps between the plants.

“A lot of data will need to be integrated and analyzed,” Yang added. “This is the first year of the project and we have a learning process. Timely capture of UAV images for early rice growth was difficult due to the small size of rice seedlings and windy weather conditions. There is a limited window when you can fly.

The team will also work to develop machine-learning algorithms that can identify key traits and identify the best-performing rice genotypes. The project will focus on key phenotypic characteristics, including stand establishment, biomass growth, final grain yield, and phenological development.

“We will develop automated algorithms that can extract phenotypic traits from UAV images taken during critical stages of rice, including planting, germination, flowering, grain filling, and maturation,” Yang said. “A digital rice selection system will be developed by integrating several traits to identify the best-performing genotypes.

Researchers believe another feature of UAV technology could be monitoring plant growth to control nitrogen and detect disease, explained Fugen Dou, a researcher at AgriLife Research.

“This proposed project is a major effort to provide an integrated UAV image-based decision-making system for rice growers and researchers,” Yang concluded. “It will be an indispensable tool for greatly improving the efficiency of rice cultivation and phenotyping.

Read more about rice progress:

New medium density SNP panel developed for US rice

Japanese researchers have unveiled measures to improve rice production

Provivi and Syngenta Crop Protection launch pheromone-based Nelvium to control harmful rice pests

A Philippine researcher discovers a rice drought-resistant gene

Godfrey Kemp

"Bacon fanatic. Social media enthusiast. Music practitioner. Internet scholar. Incurable travel advocate. Wannabe web junkie. Coffeeaholic. Alcohol fanatic."

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