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Precision Horticulture
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
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Authors
Aguilar, J
Ahamed, T
Al-buasidi, H
Albrigo, L.G
Alchanatis, V
Amakor, X
Araujo, A.G
Arikapudi, R
Arriaza, O.E
Bajwa, S.G
Balasundram, S.K
Balkcom, K.S
Barnes, W
Beck, D.L
Becker, M
Bedard, F
Belasque Jr., J
Bell, G.E
Bell, G.E
Blanke, M.M
Bloch, I
Boisgontier, D
Boisgontier, D
Bonfil, D.J
Bonfil, D.J
Bonfil, D.J
Bouhlel, N
Caballero-Novella, J.J
Caballero-Novella, J.J
Cardon, G.E
Casiano, P.M
Casiero, D.P
Castillejo-Gonz, I
Castillejo-Gonz, I
Choi, D
Chung, S
Claupein, W
Clay, D.E
Clay, S.A
Cohen, O
Cohen, Y
D, M.E
Damerow, L.M
De Kleine, M
Deng, L
Dima, C
Dima, C.S
Dobbins, R
Dobers, S.E
Doubledee, M
Duhachek, G
Ehsani, R
Ehsani, R
Ehsani, R
Ehsani, R
Elhaddad, A
Elkins, R
Fernandes, B.B
Friedrich, J
Fulton, J.P
Göttinger, M
Gao, L
Garc, A
Garc, A
Garcia, L
Garcia-Torres, L
Garcia-Torres, L
Gitelson, A.A
Gomez-Candon, D
Gomez-Candon, D
Gomez-Casero, M
Gonzalez-Mora, J
Gozdowski, D
Graeff, S
Guerra, S.P
Haghverdi, A
Hamza, N
Happich, G
Hawks, A
Herrmann, I
Herrmann, I
Herrmann, I
Hinck, S
Hirakawa, A
Hirakawa, A.R
Hoffmann, W.C
Holland, K.H
Holland, K.H
Hongo, C
Hu, J
Huang, W
Huang, Y
Huang, Y
Huh, Y
Hutchinson, A
Inamasu, R
Ingels, C
Jacobson, A.R
Jayasuriya, H.P
Jiang, Y
Jimenez, F.J
Johann, A.L
Jurado-Exp, M
Jurado-Exp, M
Kabir, M.S
Karkee, M
Karnieli, A
Karnieli, A
Karnieli, A
Khosro Anjom, F
Kim, H
Kim, Y
Klingner, S
Kumar, A
Lacey, R
Lagarrigue, M
Lagarrigue, M
Lanças, K.P
Lanças, K.P
Lan, Y
Lee, W
Lee, W
Lee, W
Leib, B.G
Lewis, K
Liaghat, S
Linker, R
Long, D.S
Lopez-Granados, F
Lopez-Granados, F
Möller, K
Ma, W
Machado, T.M
Madetoja, M
Maidl, F
Marasca, I
Marcassa, L
Martin, D
Martin, D.L
McDonald, T.P
Melnitchouck, A
Mishra, A
Morley, T.G
Moss, J.Q
Moss, J.Q
Moss, J.Q
Moulton, P
Nielsen, K
Nielsen, M.R
Niwa, K
Olsen, D.R
Orensanz, J
Orensanz, J
Ortiz, B
Ortiz, B.V
Pérez Ruiz, M
Pan, X
Pantel, M
Pate, G.L
Payne, A
Payton, M.E
Pe, J.M
Pe, J.M
Perret, J.S
Pfenning, J
Pimstein, A
Pimstein, A
Poncet, A
Porto, A
Pourreza, A
Prassack, L
Reddy, K
Reese, C.L
Reichert, G
Rigney, J.D
Roka, F
Rossant, F
Rossant, F
Ruckelshausen, A
Rupe, J.C
Sadeque, Z
Samborski, S.M
Sankaran, S
Santos, C
Schepers, J
Schneider, M.F
Scholz, C
Sclemmer, M.R
Seehuber, C
Shahinian, M
Shapira, U
Slaughter, D.C
Smith, J
Solie, J.B
Song, M
Sousa, R
Spadim, E.R
Stone, M.L
Streeter, C.R
Strenner, M
Tabile, R
Teirab, A
Testa, J
Thomson, S.J
Tian, L
Tian, Y
Ting, K
Toledo, A.D
Vallespi Gonzalez, C
Verma, U
Virk, S.S
Virrankoski, R
Vougioukas, S.G
Walsh, K
Wetterich, C
Xiong, Y
YI, S
Yang, C
Yang, C
Yang, C
Yang, C
Zach, D
Zaier, R
Zaman, Q.U
Zekri, S
Zhang, H
Zhang, Q
Zhang, X
Zhang, Y
Zhao, B
Zhao, C
Zhao, C
Zia, S
kulkarni, S.S
Topics
Precision Horticulture
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
Type
Oral
Poster
Year
2010
2014
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Filter results9 paper(s) found.

1. Near Real-time Meter-resolution Airborne Imagery For Precision Agriculture: Aerocam

Precision agriculture often relies on high resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require meter-solution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collabor... X. Zhang, C.R. Streeter, H. Kim, D.R. Olsen

2. Sectioning And Assessment Remote Images For Precision Agriculture: The Case Of Orobanche Crenate In Pea Crop

  The software SARI® has been developed to implement precision agriculture strategies through remote sensing imagery. It is written in IDL® and works as an add-on of ENVI®. It has been designed to divide remotely sensed imagery into “micro-images”, each corresponding to a small area (“micro-plot”), and to determine the quantitative agronomic and/or environmental biotic (i.e. weeds, pathogens) and/or non-biotic (i.e. nutrient levels) indicator... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, M. Gomez-casero, J.M. Pe, M. Jurado-exp, F. Lopez-granados, I. Castillejo-gonz, A. Garc

3. Multi, Super Or Hyper Spectral Data, The Right Way From Research Toward Application In Agriculture

Remote sensing provides opportunities for diverse applications in agriculture. One consideration of maximizing the utility of these applications, is the need to choose the most efficient spectral resolution. Picking the optimal spectral resolutions (multi, super or hyper) for a specific application is also influenced by other factors (e.g., spatial and temporal resolutions) of the utilized device. This work focuses mainly ... D.J. Bonfil, I. Herrmann, A. Pimstein, A. Karnieli

4. Assessment Of Field Crops Leaf Area Index By The Red-edge Inflection Point Derived From Venus Bands

The red-edge region of leaves spectrum (700-800 nm) corresponds to the spectral region that connects the chlorophyll absorption in the red and the amplified reflectance caused by the leaf structure in the near infrared (NIR) parts of the spectrum. At the canopy level, the inflection point of the red-edge slope is influenced by the plant’s condition that is related to several properties, including Leaf Area Index (LAI) and plant nutritional ... I. Herrmann, A. Pimstein, A. Karnieli, Y. Cohen, V. Alchanatis , D.J. Bonfil

5. 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 gr... T. Ahamed, L. Tian, Y. Zhang, Y. Xiong, B. Zhao, Y. Jiang, K. Ting

6. Artificial Neural Network Techniques To Predict Orange Spotting Disease In Oil Palm

       Large-Scale oil palm plantations require timely detection of disease symptoms to enable effective intervention. Orange spotting is an emerging disease that significantly reduces oil palm productivity. Remote sensing technology offers the means to detect crop biophysical properties, including crop stress, in a cost effective and non destructive manner. In this study, different portable sensors were used to measure spectral reflectance and chlorop... S. Liaghat, S.K. Balasundram

7. Management Of Remote Imagery For Precision Agriculture

Satellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack

8. Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field Mapping

  A wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by sy... Y. Lan, H. Zhang, C. Yang, D. Martin, R. Lacey, Y. Huang, W.C. Hoffmann, P. Moulton

9. Nitrogen And Water Stress Impacts Hard Red Spring Wheat (Triticum Aestivum) Canopy Reflectance

  Remote sensing-based in-season N recommendations have been proposed as a technique to improve N fertilizer use efficiency. Remote sensing estimation of South Dakota hard red spring wheat N requirements needs assessment. Research objectives were: (1) determine the effect of an in-season N application on grain yield, yield loss to nitrogen stress (YLNS), and grain protein; and (2) assess if remote sensing collected at different growth stages may be used to predict yie... C.L. Reese, D.E. Clay, D.L. Beck, S.A. Clay, D.S. Long, M. Shahinian