Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Hansen, N.P

Buana, I

Echer, F.R

» Add more authors

Add filter to result:

Authors

Canciani, M

Han, E

Jørgensen, U

Ikeda, Y

Hansen, N.P

Jensen, S.K

Weisbjerg, M.R

Didion, T

Rorato, A

Zolin, P.

Scarpin, G.J

Deponti, L.P

Echer, F.R

Bastos, L

Moura Bueno, J.

Rech, L.F

Diniz Dalmolin, R.S

de Paula Amaral, L

Buana, I

De Araujo Pedron, F

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

UAV-Based Scouting, Imaging, and Targeted Applications

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Poster

Oral

Year

2026

Home » Authors » Results

Authors

Filter results3 paper(s) found.
1. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery Applications In animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion Download paper
2. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos Download paper
3. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron

Filter results3 paper(s) found.

1. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery Applications

In animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion

Download paper

2. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution

Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos

Download paper

3. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates

The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron