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ANOUMOU, D.A

ARAI, Y

Amapu, I

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Authors

Aduramigba-Modupe, V

Amapu, I

Walsh, M

Scott, B

Ganyo, K.K

ABLEDE , K.A

KOUDJEGA, K

ANI, S

AFAWOUBO, K

ANOUMOU, D.A

MENSAH, A.T

ASSIH-FARAM, E

TCHALLA-KPONDJI, M

KPEMOUA, K

LOMBO, Y

MORIMOTO, E

ARAI, Y

NONAMI, K

ITO, T

Topics

Precision Nutrient Management

Adoption of Precision Agriculture

Type

Oral

Year

2020

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Filter results3 paper(s) found.
1. Methodology for Assessing Nutrient Status of Nigeria Croplands: AfSIS/NiSIS Pilot Project - Pathway for Precision Agriculture Mapping Inherently low soil fertility, nutrient imbalances and accelerating degradation constitute threats to precision agriculture (PA), agricultural productivity and ecosystem services in sub-Saharan Africa (Nigeria inclusive). Presently, the geographical extent of existing nutrient constraints, location specific trends and opportunities for managing these over time are highly uncertain. The AfSIS/NiSIS project assessment aims to provide spatially explicit observations, measurements and predictions... V. Aduramigba-modupe, I. Amapu, M. Walsh, B. Scott
2. Soil fertility mapping of Dry savannah zone of Togo Increasing agricultural productivity and therefore the production requires a good knowledge of the soil fertility status and a sustainable nutrients management. The objective of this study is to map spatial distribution of some selected soil fertility parameters in the dry savannah agro-ecological zone that covers the regions of Savanes and Kara in Togo. Soil fertility parameters such as pH, available phosphorus (P), exchangeable potassium (K) and organic matter were determined in soil samples... K.K. Ganyo, K.A. Ablede , K. Koudjega, S. Ani, K. Afawoubo, D.A. Anoumou, A.T. Mensah, E. Assih-faram, M. Tchalla-kpondji, K. Kpemoua, Y. Lombo 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. Methodology for Assessing Nutrient Status of Nigeria Croplands: AfSIS/NiSIS Pilot Project - Pathway for Precision Agriculture Mapping

Inherently low soil fertility, nutrient imbalances and accelerating degradation constitute threats to precision agriculture (PA), agricultural productivity and ecosystem services in sub-Saharan Africa (Nigeria inclusive). Presently, the geographical extent of existing nutrient constraints, location specific trends and opportunities for managing these over time are highly uncertain. The AfSIS/NiSIS project assessment aims to provide spatially explicit observations, measurements and predictions... V. Aduramigba-modupe, I. Amapu, M. Walsh, B. Scott

2. Soil fertility mapping of Dry savannah zone of Togo

Increasing agricultural productivity and therefore the production requires a good knowledge of the soil fertility status and a sustainable nutrients management. The objective of this study is to map spatial distribution of some selected soil fertility parameters in the dry savannah agro-ecological zone that covers the regions of Savanes and Kara in Togo. Soil fertility parameters such as pH, available phosphorus (P), exchangeable potassium (K) and organic matter were determined in soil samples... K.K. Ganyo, K.A. Ablede , K. Koudjega, S. Ani, K. Afawoubo, D.A. Anoumou, A.T. Mensah, E. Assih-faram, M. Tchalla-kpondji, K. Kpemoua, Y. Lombo

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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