Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts

Manley, Aurora; Ravelombola, Waltram; Cason, John M.; Bennett, Brian D.; Pham, Hanh; Kimura, Emi; Ruhl, Caroline; Ahmad, Waqas; Brown, Madeline

doi:10.4236/ajps.2023.143027

Cited by 3 publications

(1 citation statement)

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“…For machine learning models to be useful in agricultural research, they must be able to reliably provide robust estimates of yield and other key parameters. Phenotypes extracted at an elementary level from field plots in this study and others have been directly correlated with yield, but none of these showed a strong or consistent enough correlation to be reliably predictive on their own, indicating the need for more sophisticated ML methodologies ( Manley et al., 2023 ). Across the entire dataset, the performance of the RF and XGBoost models constructed in this study was superior to those seen in previous studies in peanut ( Balota and Oakes, 2016 ; Bagherian et al., 2023 ; Shahi et al., 2023 ).…”

Section: Discussionmentioning

confidence: 67%

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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