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Wallor, E

White, S.N

Wang, C

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Authors

Gebbers, R

Dworak, V

Mahns, B

Weltzien, C

Büchele, D

Gornushkin, I

Mailwald, M

Ostermann, M

Rühlmann, M

Schmid, T

Maiwald, M

Sumpf, B

Rühlmann, J

Bourouah, M

Scheithauer, H

Heil, K

Heggemann, T

Leenen, M

Pätzold, S

Welp, G

Chudy, T

Mizgirev, A

Wagner, P

Beitz, T

Kumke, M

Riebe, D

Kersebaum, C

Wallor, E

Li, D

Jiang, H

Chen, S

Wang, C

Hennessy, P.J

Esau, T.J

Schumann, A.W

Farooque, A.A

Zaman, Q.U

White, S.N

Topics

Precision Nutrient Management

In-Season Nitrogen Management

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2016

2018

2022

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Authors

Filter results3 paper(s) found.
1. Integrated Approach to Site-specific Soil Fertility Management In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor Download paper
2. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang Download paper
3. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White Download paper

Filter results3 paper(s) found.

1. Integrated Approach to Site-specific Soil Fertility Management

In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor

Download paper

2. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data

This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang

Download paper

3. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry

Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White

Download paper