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Huang, W
Anderson, L
Weinhold, B
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Authors
Zhang, C
Xue, X
Chen, L
Huang , W
Chen, L
Zhao, C
Huang, W
Chen, T
Wang , J
Caron, J
Anderson, L
Sauvageau, G
Gendron, L
Vail, B
Oster, Z
Weinhold, B
Topics
Engineering Technologies and Advances
Modeling and Geo-statistics
Precision Horticulture
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2010
2018
2024
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Filter results4 paper(s) found.

1. Design Of A Data Acquisition System For Weighing Lysimeters

The weighing lysimeter is an important tool for scientists to conduct... C. Zhang, X. Xue, L. Chen, W. Huang

2. Application Of Algebra Hyper-curve Neural Network In Soil Nutrient Spatial Interpolation

Study on spatial variability of soil nutrient is the basis of soil nutrient management in precision agriculture. For study on application potential and characteristics of algebra hyper-curve neural network(AHNN) in delineating soil properties spatial variability and interpolation, total 956 soil samples were taken for alkaline hydrolytic nitrogen measurement from a 50 hectares field using 20m*20m grid sampling. The test data set consisted of 100 random samples extracting... L. Chen, C. Zhao, W. Huang, T. Chen, J. Wang

3. Real Time Precision Irrigation with Variable Setpoint for Strawberry to Generate Water Savings

Water is a precious resource that is becoming increasingly scarce as the population grows and water resources are depleted in some locations or under increased control elsewhere, due to local availability or groundwater contamination issues. It obviously affects strawberry (Fragaria x ananassa Duch.) production in populated areas and water cuts are being imposed to many strawberry growers to save water, with limited information on the impact on crop yield. Precision irrigation technologies are... J. Caron, L. Anderson, G. Sauvageau, L. Gendron

4. Generative Modeling Method Comparison for Class Imbalance Correction

An image dataset, for use in object detection of hay bales, with over 6000 images of both good and bad hay bales was collected.  Unfortunately, the dataset developed a class imbalance, with more good bale images than bad bales.  This dataset class imbalance caused the bad bale class to over train and the good bale class to under train, severely impacting precision, and recall.  To correct this imbalance and provide a comparison of differing generative modeling methods; three different... B. Vail, Z. Oster, B. Weinhold