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Liburd, O.E
Boyko, Y.I
Tikasz, P
Morales, G
Saleem, S.R
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Authors
Boyko, Y.I
Adamchuk, V.I
Khan, F.S
Zaman, Q.U
Schumann, A.W
Madani, A
Percival, D.C
Farooque, A.A
Saleem, S.R
Khan, F.S
Tikasz, P
Buelvas, R.M
Lefsrud, M
Adamchuk, V
Morales, G
Sheppard, J.W
Peerlinck, A
Hegedus, P
Maxwell, B
Zhou, C
Lee, W
Pourreza, A
Schueller, J.K
Liburd, O.E
Ampatzidis, Y
Zuniga-Ramirez, G
Topics
Spatial Variability in Crop, Soil and Natural Resources
Precision Horticulture
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2018
2022
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1. Analysis of Spatial Variability of Key Soil Attributes In North-Central Ukraine

As Ukrainian agricultural production undergoes major changes, a better understanding of the diversity of land resources is needed to optimize management.  Dealing with large fields (over 100 ha in size) with non-uniform growing conditions presents an opportunity for site-specific management of agricultural inputs. This publication describes our 2010 pilot study on the implementation of integrated mapping of apparent soil electrical conductivity and field topography to guide soil sampling... Y.I. Boyko, V.I. Adamchuk

2. Relationship of Soil Properties to Apparent Ground Conductivity in Wild Blueberry Fields

  One of the fundamental deficiencies in high value crops is the lack of detailed, up-to-date and pertinent geo-referenced soil information for site-specific crop management to improve productivity. This experiment was designed to estimate and map soil properties rapidly and reliably using an electromagnetic induction (EMI) method. Two wild blueberry... F.S. Khan, Q.U. Zaman, A.W. Schumann, A. Madani, D.C. Percival, A.A. Farooque, S.R. Saleem, F.S. Khan

3. Implementation of a CAN Bus System to Monitor Hydroponic Systems

Controlled Area Network (CAN) bus systems designed for greenhouse monitoring have been proposed to measure soil moisture content, yet they are still absent from hydroponic systems. In this study, irrigation control, monitoring of substrate moisture levels and temperature were achieved using a CAN bus system connected to hydroponic beds. In total, five nodes were mounted on five hydroponic beds and two irrigation methods were compared on lettuce and kale: first, where a pre-set timer activated... P. Tikasz, R.M. Buelvas, M. Lefsrud, V. Adamchuk

4. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep Learning

Nitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points should... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell

5. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System

Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality.  However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cameras... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez