Rice researchers at the A&M AgriLife Research Center in Beaumont, Texas, have taken rice selection and breeding to the next level with an aircraft (UAV) project. The team will use UAVs to take real-time snapshots of rice crops, extract harvest phenotypic traits from those images and analyze the information to reveal high-yielding rice genotypes, Texas A&M AgriLife said in a news release.
Researchers hope to avoid one of the main obstacles to data collection – the time-consuming and time-consuming procedure of manually collecting field data through skilled labor. Yubin Yang, a senior biosystems analyst at the Beaumont Center, will lead the project with funding from a three-year $ 650,000 grant from the USDA National Institute of Food and Agriculture (NIFA).
Key research objectives:
- Calculate the key phenotypic traits for rice growth and development.
- Record UAV images of rice genotypes at crucial stages of rice growth.
- Create advanced image processing algorithms to remove basic phenological, morphological and architectural features.
- Generate a digital rice selection system for screening for the most efficient genotypes using data integration with multi-feature decision making.
The new wave of UAV technology
“Traditional manual measurement of rice phenotypic traits takes a long, long time,” Young said. “It is becoming increasingly difficult to hire qualified and experienced staff. UAV technology and advanced image processing can potentially provide a cost-effective and reliable alternative. We can use UAVs to capture images of rice at key stages of growth and develop algorithms to extract different phenotypic traits for hundreds or even thousands of rice genotypes.
Throughout the rice harvest season, numerous UAV flights will be conducted to capture thousands of UAV images, as well as real-world real information. Different camera angles will be used to analyze the establishment of stands as well as the gaps between plants.
“Significant amounts of data will have to be integrated and analyzed,” Young added. “This is the first year of the project and for us it is a learning process. Timely capture of UAV images for early rice growth is a challenge due to the small size of rice seedlings and windy weather. There is a limited window when you can fly. “
The team will also work on developing machine learning algorithms that can identify key traits and determine the best-performing rice genotypes. The project will focus on key phenotypic traits, including plantation creation, biomass growth, final grain yield and phenological development.
“We will develop automated algorithms that can extract phenotypic traits from UAV images taken at critical stages of rice, including seedlings, cultivation, flowering, grain filling and maturity,” Young said. “The digital rice selection system will be developed by integrating multiple traits to identify the most efficient genotypes.”
Researchers believe that another feature of UAV technology may be to monitor plant growth for nitrogen control and disease detection, said Fugen Doe, a fellow scientist at AgriLife Research.
“This proposed project is a major effort to provide an integrated decision-making system based on UAV images to rice growers and researchers,” Young concluded. “This will be an indispensable tool for significantly improving rice cultivation and phenotyping efficiency.”
Read more about the progress of rice:
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Researchers in Japan have discovered tools to improve rice production
Provivi and Syngenta Crop Protection launch Nelvium based on pheromones to control harmful pests in rice
A Philippine researcher has discovered a drought resistance gene in rice