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3rd African Conference on Precision Agriculture (AfCPA)

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Authors

ADJE, C

Abdallah, O

Abdellah, E

Aboneh, T

Abrahams, M

Achigan-Dako, E.G

Adefare, T

Adegbayi, R

Adolwa, I

Aduramigba-Modupe, V

Ali, A

Alshihabi, O

Amri, M

Angar, H

Anken, T

Atnafu, O

Atyosi, Y

Balemi, T

Bamigboye, F

Bayad, M

Ben-Dor, E

Boughattas, N

Brou, I.E

Buthelezi, S

Bwambale, E

Bühlmann, A

Cabaraux, J

Cammarano, D

Chebli, Y

Chentouf, M

Chinwa, H

Cook, S

Dlamini, C

Durojaiye, A.M

EL KHALFI, C

ENDRIS, S

El Otmani, S

El-Metwally, E

Elame, F

Engoke, C

FASSINOU HOTEGNI, V

Fadairo, O

Felicioni, S

Frimpong, K

Gachene, C.K

Gerrard, B

Gitau, M

Gitau, T

Gumede, N

HAJJAJ, H

HARKANI, A

HOUESSOU, G

HOUMAIRI, H

Hafedh, H

Hess, P

Hmimou, M

Houdegbe, C

IBN EL MOKHTAR, F

IBNO NAMR, K

IDRISSI, O

Ikabat, J

Imen, H

KINDIKIZA, D

KRIMIBENCHEQROUN, S

Kabenge, I

Kamutando, C.N

Karangwa, A

Kehinde, I

Keli, A

Kenea, W

Kironchi, G

Kitonyo, O

Kouassi, K

LINKPON, O

Landolsi, M

Lesueur, C

Li, R

Liang, W

Lund, E

Lutomia, C

MATILA, T

MECHRI, M

MUGI, E

Majumdar, K

Mandumbu, R

Manyatsi, A

Marwa, K

Masemola, R

Matongera, T

Mesfin, T

Mohamed, Z

Mongína, D

Musinguzi, P

Mutanga, O

Mutegi, J

Muthamia, J

Mutsamba-Magwaza, E

Muyonga, J

Nakawuka, P

Ndlovu, S

Nigussie, A

Njoroge, S

Nouiri, I

Nyagumbo, I

Nyamadzawo, G

Nyamayevu, D

Nyawasha, R

Nyikal, R.A

OYEDEPO, P

Odindi, J

Ojambo, E

Olutoberu, T

Oyedepo, E

Persson, K

Phillips, S

Popoola, S

Rabiu Garba, A

Rorissa, P

SEHBAOUI, F

Sabri, A

Safi, U

Saratu Usman, I

Sawsen, A

Sayed, R

Sbai, I

Sibanda, M

Soaud, A

Soumaya, A

Söderström, M

TAMALE, P

Tamiru, S

Taylor, J

Tchokponhoué, D

Toviho, J

Wanyama, J

Woldekristos, A.N

Youssef, T

Yusufu, F

Zewdie Gebrekidan, B

Zingore, S

musa, S

Topics

Adoption of Precision Agriculture

Small Holders and Precision Agriculture

Remote and Proximal Sensing

Precision Planting/Harvesting

Water Management for Precision Agriculture

On-Farm Experimentation

Precision Nutrient Management

Precision Livestock Management

Social Science Applications in Precision Agriculture

Decision Support Systems, Software and Mobile Applications

Education and Engagement in Precision Agriculture

Artificial Intelligence (AI) in Agriculture

Geospatial Data and Mapping

Climate Smart Agriculture

Plenary Session

Keynote

Robotics, Automation, and Small Farm Mechanization

Type

Oral

Poster

Year

2024

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Conference

Filter results2 paper(s) found.
1. High-Throughput Field Phenotyping of Ascochyta Blight Disease Severity in Chickpea Using Multispectral Imaging Ascochyta blight (AB) caused by Ascochyta rabiei (Pass.) Labr. is an important and widespread disease of chickpea (Cicer arietinum L.) worldwide. The disease is particularly severe under cool and humid weather conditions, leading to crop losses at all stages of chickpea growth. Screening for resistant cultivars remains the most effective, economical and ecological method of disease management. However, traditional phenotyping methods that relying on trained experts are... F. Ibn el mokhtar, S. Krimibencheqroun , , A. Harkani , H. Houmairi , O. Idrissi , E. Abdellah , E. Abdellah Download paper
2. The Use of Unmanned Aerial Vehicle (UAV) Remotely Sensed Data and Biophysical Variables to Predict Maize Above-Ground Biomass (AGB) in Small-Scale Farming Systems Considering the current and projected increase in human population, approaches to optimize crop productivity to meet the rising demand are paramount. Timely and accurate maize Above Ground Biomass (AGB) measurements allow for development of models that can precisely predict yield prior to harvesting, useful for food production management and sustenance. The development of Unmanned Aerial Vehicles (UAVs) as a new generation of robust remote sensing platforms, mounted with high-resolution senso... C. Dlamini, J. Odindi, O. Mutanga, T. Matongera Download paper

Filter results2 paper(s) found.

1. High-Throughput Field Phenotyping of Ascochyta Blight Disease Severity in Chickpea Using Multispectral Imaging

Ascochyta blight (AB) caused by Ascochyta rabiei (Pass.) Labr. is an important and widespread disease of chickpea (Cicer arietinum L.) worldwide. The disease is particularly severe under cool and humid weather conditions, leading to crop losses at all stages of chickpea growth. Screening for resistant cultivars remains the most effective, economical and ecological method of disease management. However, traditional phenotyping methods that relying on trained experts are... F. Ibn el mokhtar, S. Krimibencheqroun , , A. Harkani , H. Houmairi , O. Idrissi , E. Abdellah , E. Abdellah

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2. The Use of Unmanned Aerial Vehicle (UAV) Remotely Sensed Data and Biophysical Variables to Predict Maize Above-Ground Biomass (AGB) in Small-Scale Farming Systems

Considering the current and projected increase in human population, approaches to optimize crop productivity to meet the rising demand are paramount. Timely and accurate maize Above Ground Biomass (AGB) measurements allow for development of models that can precisely predict yield prior to harvesting, useful for food production management and sustenance. The development of Unmanned Aerial Vehicles (UAVs) as a new generation of robust remote sensing platforms, mounted with high-resolution senso... C. Dlamini, J. Odindi, O. Mutanga, T. Matongera

Download paper