首页|U.S. Department of Agriculture (USDA) Researchers Target Machine Learning (Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms)
U.S. Department of Agriculture (USDA) Researchers Target Machine Learning (Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Frontiers Media Sa
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on artificial intelligence is the subject of a new report. According to news reporting from Lubbock, Texas, by NewsRx journalists, research stated, “Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop’s genetic gain rate.” The news correspondents obtained a quote from the research from U.S. Department of Agriculture (USDA): “Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes.”
U.S. Department of Agriculture (USDA)LubbockTexasUnited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging TechnologiesMachine Learn- ingRemote Sensing
NETL
NSTL
Frontiers Media Sa
