Proceedings :: International Society of Precision Agriculture

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Koch, G

Silva, J.P

Sárvio Valente, D

Smith, E

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Authors

Javed, B

Cambouris, A

Smith, E

Dandrifosse, S

Ziadi, N

Karam, A

Oliveira, R.P

Silva, J.P

Martins, J.V

Dos Santos, R

Barbosa, M

Oliveira, L

Antônio, M

Koch, G

Moura, P.A

Silva, F

Santana, C.C

Topics

Precision Agriculture for Sustainability and Environmental Protection

Precision Horticulture and Specialty Crop Management

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2026

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Filter results3 paper(s) found.
1. Machine Learning–driven Insights into Nitrogen Dynamics and Greenhouse Gas Emissions in Potato Production Systems Nitrogen (N) is an essential nutrient for potato vegetative growth, yet it remains one of the most challenging elements to manage in modern agricultural systems. Despite its critical role in crop productivity, excessive or poorly timed N application can lead to significant environmental losses, particularly through groundwater nitrate (NO3-N) leaching and emissions of nitrous oxide (N2O), a greenhouse gas approximately 300 times more potent than CO2. Therefore,... B. Javed, A. Cambouris, E. Smith, S. Dandrifosse, N. Ziadi, A. Karam Download paper
2. Integrated Evaluation of Precision Spraying Systems in Pecan Orchards: Smart Apply System™, Conventional Airblast, and Drone Application Pecan production in Georgia faces increasing challenges related to weather variability, rising labor costs, and higher prices of agricultural inputs, particularly fungicides required for scab (Venturia effusa) management. Conventional air-assisted sprayers operate at fixed application rates and do not account for tree size variability or canopy gaps, often resulting in overapplication and reduced efficiency. Emerging precision technologies, such as Smart Apply™ sprayers and spray... R.P. Oliveira, J.P. Silva, J.V. Martins, R. Dos Santos, M. Barbosa, L. Oliveira
3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana Download paper

Filter results3 paper(s) found.

1. Machine Learning–driven Insights into Nitrogen Dynamics and Greenhouse Gas Emissions in Potato Production Systems

Nitrogen (N) is an essential nutrient for potato vegetative growth, yet it remains one of the most challenging elements to manage in modern agricultural systems. Despite its critical role in crop productivity, excessive or poorly timed N application can lead to significant environmental losses, particularly through groundwater nitrate (NO3-N) leaching and emissions of nitrous oxide (N2O), a greenhouse gas approximately 300 times more potent than CO2. Therefore,... B. Javed, A. Cambouris, E. Smith, S. Dandrifosse, N. Ziadi, A. Karam

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2. Integrated Evaluation of Precision Spraying Systems in Pecan Orchards: Smart Apply System™, Conventional Airblast, and Drone Application

Pecan production in Georgia faces increasing challenges related to weather variability, rising labor costs, and higher prices of agricultural inputs, particularly fungicides required for scab (Venturia effusa) management. Conventional air-assisted sprayers operate at fixed application rates and do not account for tree size variability or canopy gaps, often resulting in overapplication and reduced efficiency. Emerging precision technologies, such as Smart Apply™ sprayers and spray... R.P. Oliveira, J.P. Silva, J.V. Martins, R. Dos Santos, M. Barbosa, L. Oliveira

3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery

Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana

Download paper