Exploratory Data Analysis on Blueberry yield through Bayes and Function Models
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Abstract
Agricultural researchers are using machine learning to predict crop yield. Many machine learning algorithms need lots of data. One of the major challenges in training and experimenting with machine learning algorithms is the availability of training data in sufficient quality and quantity remains a limiting factor. The Linear Discriminant Analysis produces 95.88% of accuracy which is most efficient of selected models; The Nave Bayes Multinomial has 69.88% accuracy, while the Linear Discriminant Analysis has 0.96 precision. The NBM has 0.71 precision, while Linear Discriminant Analysis has 0.95 recall. The Linear Discriminant Analysis produces 0.99 of ROC, which is the most efficient outcome of selected models. The NBM gives least ROC, which is 0.80. The Linear Discriminant Analysis produces 0.99 of PRC, which is the most efficient outcome of selected models. The NBM gives least PRC, which is 0.72. The LDA explores efficient outcome with low deviations. Four machine-learning-based predictive models were then built using the simulated dataset. This simulated data provides researchers with actual field observation data and those who want to test machine learning algorithms' response to real data with crop yield prediction models.
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