A statistical evaluation of replicated block designs and spatial variability in sorghum performance trials

Pugh, N. Ace; Morgan, Cristine L.S.; Schnell, Ronnie W.; Horn, Katrina; Pietsch, D.; Rooney, William L.

doi:10.1080/15427528.2019.1627686

Cited by 1 publication

(1 citation statement)

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“…Spatial soil and environmental data may be key elements that could greatly increase the efficacy of ML models ( Pandith et al., 2020 ). Data for the presence of pests, the weather, the soil, and many other biotic and abiotic factors could assist ML models in producing reliable predictions ( Sudduth et al., 1996 ; Pugh et al., 2019 ). Despite the absence of these data in the present study, our results demonstrate that peanut breeders and researchers can use RF and XGBoost models to make program selections if they use appropriate training data.…”

Section: Discussionmentioning

confidence: 99%

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms

Pugh,

Young,

Ojha

et al. 2024

Front. Plant Sci.

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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. 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. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

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Section: Discussionmentioning

confidence: 99%