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ARAI, Y

SATO, S

Elsayed , M

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Authors

Belal, A

Elsayed , M

Jalhoum, M.E

Abdelatif , M

Hendawy , E

Emam, M

Zahran , M

MORIMOTO, E

LEE, J

NONAMI, K

MATUMURA, I

IKEBE, M

SATO, S

MORIMOTO, E

ARAI, Y

NONAMI, K

ITO, T

Topics

Adoption of Precision Agriculture

Precision Agriculture for Field and Plantation Crops

Type

Oral

Year

2020

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Filter results3 paper(s) found.
1. Precision Farming Technology to Increase Soil and Crop Productivity in Egypt Using Remote Sensing and GIS Precision farming or site-specific land management is a new approach for development the agriculture processes to increase the soil and crop productivity with saving efforts and costs. Precision farming includes many techniques such as Global Position Systems (GPS), Geographic Information Systems (GIS), Remote Sensing (RS), Yield Monitors, Internet of Things (IOT), Variable Rate Application (VRA), Yield Mapping, Site-Specific Management Zones (SSMZ) and Crop Modeling. SSMZ delineation can be improved... A. Belal, M. Elsayed , M.E. Jalhoum, M. abdelatif , E. Hendawy , M. Emam, M. Zahran
2. Development of Canopy Mapping System of Asian pears (Pyrus pyrifolia Naka) Using Terrestrial Laser Scanning In this paper, the canopy mapping system (CMS) of Asian pears for estimating yield during Bud thinning and Pruning operations using point cloud data was proposed. Bud thinning and Pruning in Asian pear (Pyrus pyrifolia Naka) is necessary to ensure quality and yield but is time-consuming and heavily depends on work knowledge. This study described a method of estimating the number of fruits through the length of a branch based on remote sensing. The CMS would be useful to support more efficient... E. Morimoto, J. Lee, K. Nonami, I. Matumura, M. Ikebe, S. Sato Download paper
3. Development of Lodging Direction Determination System Using Image Processing In this study, image processing system was developed for application on rice plants to determine lodging condition, which was contributing factor to declining harvester efficiency by using combine harvester. Therefore, We developed a system for determination of the lodging direction by algorithm based on convolutional neural network (CNN). As for deep learning framework, Pytorch1.1.0 were used to train and test the judging direction. GoogLeNet was used as a pre-trained CNN model. Lodging... E. Morimoto, Y. Arai, K. Nonami, T. Ito Download paper

Filter results3 paper(s) found.

1. Precision Farming Technology to Increase Soil and Crop Productivity in Egypt Using Remote Sensing and GIS

Precision farming or site-specific land management is a new approach for development the agriculture processes to increase the soil and crop productivity with saving efforts and costs. Precision farming includes many techniques such as Global Position Systems (GPS), Geographic Information Systems (GIS), Remote Sensing (RS), Yield Monitors, Internet of Things (IOT), Variable Rate Application (VRA), Yield Mapping, Site-Specific Management Zones (SSMZ) and Crop Modeling. SSMZ delineation can be improved... A. Belal, M. Elsayed , M.E. Jalhoum, M. abdelatif , E. Hendawy , M. Emam, M. Zahran

2. Development of Canopy Mapping System of Asian pears (Pyrus pyrifolia Naka) Using Terrestrial Laser Scanning

In this paper, the canopy mapping system (CMS) of Asian pears for estimating yield during Bud thinning and Pruning operations using point cloud data was proposed. Bud thinning and Pruning in Asian pear (Pyrus pyrifolia Naka) is necessary to ensure quality and yield but is time-consuming and heavily depends on work knowledge. This study described a method of estimating the number of fruits through the length of a branch based on remote sensing. The CMS would be useful to support more efficient... E. Morimoto, J. Lee, K. Nonami, I. Matumura, M. Ikebe, S. Sato

Download paper

3. Development of Lodging Direction Determination System Using Image Processing

In this study, image processing system was developed for application on rice plants to determine lodging condition, which was contributing factor to declining harvester efficiency by using combine harvester. Therefore, We developed a system for determination of the lodging direction by algorithm based on convolutional neural network (CNN). As for deep learning framework, Pytorch1.1.0 were used to train and test the judging direction. GoogLeNet was used as a pre-trained CNN model. Lodging... E. Morimoto, Y. Arai, K. Nonami, T. Ito

Download paper