Wild Blueberry Yield Prediction

Waqas Ali

wildBB_yield

Project:

Wild Blueberry Yield Prediction

Description:

The dataset used for predictive modelling was generated by the Wild Blueberry Pollination Simulation Model, which is an open-source, spatiallyexplicit computer simulation program, that enables exploration of how various factors, including plant spatial arrangement, outcrossing and self-pollination, bee species compositions and weather conditions, in isolation and combination, affect pollination eficiency and yield of the wild blueberry agroecosystem. The simulation model has been validated by the field observation and experimental data collected in Maine USA and Canadian Maritimes during the last 30 years and now is a useful tool for hypothesis testing and theory development for wild blueberry pollination researches. This simulated data provides researchers who have actual data collected from field observation and those who wants to experiment the potential of machine learning algorithms response to real data and computer simulation modelling generated data as input for crop yield prediction models. The dataset and Research paper are provided into the drive … Problem Statement: The target feature is yieldwhich is a continuous variable. The task is to classify this variable based on the other 17 features step-by-step by going through each day's task. The evaluation metrics will be RMSE score

What is IQR?

Wikipedia Definition
The interquartile range (IQR), also called the midspread or middle 50%, or technically H-spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles, IQR = Q3 − Q1
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Posted Mar 18, 2025

The dataset used for predictive modelling was generated by the Wild Blueberry Pollination Simulation Model