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Borůvka, L
Aggarwal, V
Warren, J.G
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Authors
Zhang, R
Chen, L
Guo, J
Warren, J.G
Warren, J.G
Gholizadeh, A
Saberioon, M
Borůvka, L
Ahmad, A
Aggarwal, V
Saraswat, D
El Gamal, A
Johal, G
Topics
Engineering Technologies and Advances
Proximal Sensing in Precision Agriculture
Applications of Unmanned Aerial Systems
Type
Oral
Year
2010
2016
2022
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Filter results3 paper(s) found.

1. Energy-efficient Wireless Sensor Network System For Soil Moisture Information Collecting

Collecting field soil moisture information is the foundation of auto-irrigation. This paper introduced a soil moisture information collecting system based on wireless sensor network (WSN) technology and with application background of automatic drip irrigation for cotton field. Firstly, application background was analyzed and application requirement was defined. The system worked together with a drip irrigation system in cotton field. After study, it was found that the output of soil moisture sensor... R. Zhang, L. Chen, J. Guo, J.G. Warren, J.G. Warren

2. Memory Based Learning: A New Data Mining Approach to Model and Interpret Soil Texture Diffuse Reflectance Spectra

Successful estimation of spectrally active soil texture with Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) spectroscopy depends mostly on the selection of an appropriate data mining algorithm. The aims of this paper were: to compare different data mining algorithms including Partial Least Squares Regression (PLSR), which is the most common technique in soil spectroscopy, Support Vector Machine Regression (SVMR), Boosted Regression Trees (BRT), and Memory... A. Gholizadeh, M. Saberioon, L. Borůvka

3. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal