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Zaman, Q.U
Zsebo, S
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Authors
Milics, G
Szabó, S
Bűdi, K
Takács, A
Láng, V
Zsebo, S
Zaman, Q.U
Farooque, A
Jamei, M
Esau, T.J
Topics
Site-Specific Nutrient, Lime and Seed Management
Big Data, Data Mining and Deep Learning
Type
Oral
Poster
Year
2018
2024
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1. Increasing Corn (Zea Mays L.) Profitability by Site-Specific Seed and Nutrient Management in Igmand-Kisber Basin, Hungary

Variable Rate Technology (VRT) in seeding and nutrient management has been developed in order to apply crop inputs variably. Farm equipment is widely available to manage in-field variability in Hungary, however, defining management zones, seed rates and amounts of nutrients is still a challenge. An increasing number of growers in Hungary have started adopting precision agriculture technology; however, data on profitability concerning site-specific seeding and nitrogen management is not widely... G. Milics, S. Szabó, K. Bűdi, A. Takács, V. Láng, S. Zsebo

2. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic Canada

The potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model called... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau