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Regression Analysis of UAV Collected Cotton Crop Data for Yield Prediction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Regression Analysis of UAV Collected Cotton Crop Data for Yield Prediction./
作者:	Lopez, Bianca Brienne.
面頁冊數:	1 online resource (73 pages)
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29167267click for full text (PQDT)
ISBN:	9798819373422

Regression Analysis of UAV Collected Cotton Crop Data for Yield Prediction.
Lopez, Bianca Brienne.

Regression Analysis of UAV Collected Cotton Crop Data for Yield Prediction. - 1 online resource (73 pages)

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.Sc.)--Texas A&M University - Corpus Christi, 2022.

Includes bibliographical references

Prediction of cotton yield can enable farmers to make more beneficial planning, budgeting, andintervention decisions. The objective of this thesis was to assess the performance of principal component regression (PCR), partial least squares regression (PLSR), Ridge regression, and least absolute shrinkage and selection operator (LASSO) regression for predicting cotton yield. During the2016 growing season, excess greenness index (ExG), normalized difference vegetation index (NDVI),canopy height (CH), and canopy volume (CV) were calculated weekly from UAS (unmanned aerialsystems) collected RGB (red, green, blue) and multispectral images of an experimental cotton fieldlocated at the Texas A&M AgriLife Research Center in Corpus Christi, Texas, USA (27◦ 46' 57.08"N, 97◦ 33' 40.94" W). Irrigation was taken as a categorical variable, with the field split into two approximately equal sections of dry and irrigated plots. Data were split into 80 percent training data and20 percent testing data for all models and a 10-fold cross validation was performed to find the optimalnumber of principal components for the PCR, latent variables for PLSR, and the hyperparameters ofthe LASSO and Ridge regressions. All models were trained with the weekly time series variablesExG, NDVI, CH, and CV and the categorical variable irrigation. Each model was also trained withthe same time series variables up to 67 days after planting and irrigation. The set of models trained onthe entire season resulted in the following test set mean squared error values and R-squared scores, respectively; PCR with ∼2.83 and ∼0.48, PLSR with ∼1.00 and ∼0.80, LASSO regression with ∼0.94and ∼0.81, and Ridge regression with ∼1.32 and ∼0.73. The models trained on the first 67 days subset obtained the following mean squared error values and R-squared scores, respectively; PCR with∼2.88 and ∼0.47, PLSR with ∼1.54 and ∼0.70, LASSO regression with ∼1.60 and ∼0.67, and Ridgeregression with ∼1.61 and ∼0.67. LASSO regression fit best out of the four regressions used to modelthe entire season's data with the highest R-squared value and lowest MSE score. This model couldbe useful for decision-making in preparation for future growing seasons. The PLSR model trained onthe first 67 days after planting subset resulted in the lowest MSE and the highest R-squared of ∼0.70.Decisions for intervention could be made with reasonable accuracy at 67 days after planting basedon the PLSR model.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798819373422Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Crop yield predictionIndex Terms--Genre/Form:

542853
Electronic books.

Regression Analysis of UAV Collected Cotton Crop Data for Yield Prediction.
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504 $a Includes bibliographical references
520 $a Prediction of cotton yield can enable farmers to make more beneficial planning, budgeting, andintervention decisions. The objective of this thesis was to assess the performance of principal component regression (PCR), partial least squares regression (PLSR), Ridge regression, and least absolute shrinkage and selection operator (LASSO) regression for predicting cotton yield. During the2016 growing season, excess greenness index (ExG), normalized difference vegetation index (NDVI),canopy height (CH), and canopy volume (CV) were calculated weekly from UAS (unmanned aerialsystems) collected RGB (red, green, blue) and multispectral images of an experimental cotton fieldlocated at the Texas A&M AgriLife Research Center in Corpus Christi, Texas, USA (27◦ 46' 57.08"N, 97◦ 33' 40.94" W). Irrigation was taken as a categorical variable, with the field split into two approximately equal sections of dry and irrigated plots. Data were split into 80 percent training data and20 percent testing data for all models and a 10-fold cross validation was performed to find the optimalnumber of principal components for the PCR, latent variables for PLSR, and the hyperparameters ofthe LASSO and Ridge regressions. All models were trained with the weekly time series variablesExG, NDVI, CH, and CV and the categorical variable irrigation. Each model was also trained withthe same time series variables up to 67 days after planting and irrigation. The set of models trained onthe entire season resulted in the following test set mean squared error values and R-squared scores, respectively; PCR with ∼2.83 and ∼0.48, PLSR with ∼1.00 and ∼0.80, LASSO regression with ∼0.94and ∼0.81, and Ridge regression with ∼1.32 and ∼0.73. The models trained on the first 67 days subset obtained the following mean squared error values and R-squared scores, respectively; PCR with∼2.88 and ∼0.47, PLSR with ∼1.54 and ∼0.70, LASSO regression with ∼1.60 and ∼0.67, and Ridgeregression with ∼1.61 and ∼0.67. LASSO regression fit best out of the four regressions used to modelthe entire season's data with the highest R-squared value and lowest MSE score. This model couldbe useful for decision-making in preparation for future growing seasons. The PLSR model trained onthe first 67 days after planting subset resulted in the lowest MSE and the highest R-squared of ∼0.70.Decisions for intervention could be made with reasonable accuracy at 67 days after planting basedon the PLSR model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a Agronomy. $3 2122783
650 4 $a Mathematics. $3 515831
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653 $a Partial least squares Regression
653 $a Principal component Regression
653 $a Ridge regression
653 $a UAV
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