2021
DOI: 10.13031/trans.14145
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Estimation of Corn Emergence Date Using UAV Imagery

Abstract: HighlightsUAV imagery can be used to characterize newly-emerged corn plants.Size and shape features used in a random forest model are able to predict days after emergence within a 3-day window.Diameter and area were important size features for predicting DAE for the first, second, and third week of emergence.Abstract. Assessing corn (Zea mays L.) emergence uniformity soon after planting is important for relating to grain production and making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been… Show more

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Cited by 2 publications
(4 citation statements)
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“…Moreover, the RMSE = 0.39 plant m -1 was similar to that of a previous study with no-till management (RMSE = 0.40 plant m -1 , Vong, Conway, et al, 2021). Another previous study (Vong, Stewart, et al, 2021) estimated individual corn plant DAE using a random forest machine learning method and showed moderate 3-day classification accuracies of < 0.85 (-1 to +1 DAE) when estimating the DAE after two weeks of emergence (DAE 13 to DAE 20). Our study showed improved results at estimating the mean of the DAE in 1-m row segments with an accuracy of 0.95 and RMSE of 1.0 day.…”
Section: Corn Emergence and Plant Spacing At Monitoring Sitessupporting
confidence: 88%
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“…Moreover, the RMSE = 0.39 plant m -1 was similar to that of a previous study with no-till management (RMSE = 0.40 plant m -1 , Vong, Conway, et al, 2021). Another previous study (Vong, Stewart, et al, 2021) estimated individual corn plant DAE using a random forest machine learning method and showed moderate 3-day classification accuracies of < 0.85 (-1 to +1 DAE) when estimating the DAE after two weeks of emergence (DAE 13 to DAE 20). Our study showed improved results at estimating the mean of the DAE in 1-m row segments with an accuracy of 0.95 and RMSE of 1.0 day.…”
Section: Corn Emergence and Plant Spacing At Monitoring Sitessupporting
confidence: 88%
“…USA). The original image was first enhanced using decorrelation stretch Vong, Stewart, et al, 2021). Then, a threshold value of 220 (tested using a trial-and-error method and visual inspection) in the green band of the enhanced image was used to remove most of the background to create binary images.…”
Section: Study Site and Ground Data Collectionmentioning
confidence: 99%
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