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Schulthess, R
Saberioon, M
Safranski, T.J
Drew, P
Zhang, Y
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Authors
Gholizadeh , A
Mohd Soom , M
Saberioon, M
Ahamed, T
Tian, L
Zhang, Y
Xiong, Y
Zhao, B
Jiang, Y
Ting, K
Schulthess, R
Schelling, K
Weist, D
Borùvka, L
Saberioon, M
Vašát, R
Gholizadeh, A
Gholizadeh, A
Saberioon, M
Borůvka, L
Drew, P
Sudduth, K.A
Sadler, E
Zhou, J
Xu, Z
Safranski, T.J
Bromfield, C
Topics
Sensor Application in Managing In-season Crop Variability
Remote Sensing Applications in Precision Agriculture
Precision A-Z for Practitioners
Proximal Sensing in Precision Agriculture
Proximal Sensing in Precision Agriculture
Sensor Application in Managing In-season Crop Variability
Precision Dairy and Livestock Management
Type
Poster
Oral
Year
2012
2010
2014
2016
2024
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Filter results7 paper(s) found.

1. Estimation of Nitrogen of Rice in Different Growth Stages Using Tetracam Agriculture Digital Camera

Many methods are available to monitor nitrogen content of rice during various growth stages. However, this monitoring still requires a quick, simple, accurate and inexpensive technique that needs to be developed. In this study, Tetracam Agriculture Digital Camera (ADC) was used to acquire high spatial and temporal resolution in order to determine the status of nitrogen (N) and predict the grain yield of rice (Oriza sativa L.). In this study, 12 pots of rice with four different N treatments (0, 125,... A. Gholizadeh , M. Mohd soom , M. Saberioon

2. Site-specific Management For Biomass Feedstock Production: Development Of Remote Sensing Data Acquisition Systems

Efficient biomass feedstock production supply chain spans from site-specific management of crops on field to the gate of biorefinery. Remote sensing data acquisition systems have been introduced for site-specific management, which is a part of the engineering solutions for biomass feedstock production. A stand alone tower remote sensing platform was developed to monitor energy crops using multispectral imagery. The sensing system was capable of collecting RGB and CIR images during the crop growing... T. Ahamed, L. Tian, Y. Zhang, Y. Xiong, B. Zhao, Y. Jiang, K. Ting

3. From Rapideye's Spad In The Sky To N Application Maps

... R. Schulthess, K. Schelling, D. Weist

4. Visible And Near-Infrared Spectroscopy For Monitoring Potentially Toxic Elements In Reclaimed Dumpsite Soils Of The Czech Republic

Due to rapid economic development, high levels of potentially harmful elements and heavy metals are continuously being released into the brown coal mining dumpsites of the Czech Republic. Elevated metal contents in soils not only dramatically impact the soil quality, but also due to their persistent nature and long biological half-lives, contaminant elements can accumulate in the food chain and can eventually endanger human health. Conventional methods for investigating potentially... L. Borùvka, M. Saberioon, R. Vašát, A. Gholizadeh

5. 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

6. Development of a Multispectral Sensor for Crop Canopy Temperature Measurement

Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One approach... P. Drew, K.A. Sudduth, E. Sadler

7. Automated Sow Estrus Detection Using Machine Vision Technology

Successful artificial insemination for gilts and sows relies on accurate timing that is determined by estrus check. Estrus checks in current farms are manually conducted by skilled breeding technicians using the back pressure test (BPT) method that is labor-intensive and inefficient due to the large animal-to-staff ratio. This study aimed to develop a robotic imaging system powered by artificial intelligence technology to automatically detect estrus status for gilts and sows in a stall-housing... J. Zhou, Z. Xu, T.J. Safranski, C. Bromfield