Proceedings :: International Society of Precision Agriculture

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Zolin, P.

Fantinel, R.A

Otterson, J

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Authors

Zolin, P.

Peranzoni Deponti, L

Bastos, L

Amaral, L.R

Nketia, K

Lotfi, A

Neudorf, S

Ha, T

Khan, A

Otterson, J

Amaral, L.D

Pereira da Silva, S.D

Fantinel, R.A

Richter, V

, N

Rorato, A

Zolin, P.

Scarpin, G.J

Deponti, L.P

Echer, F.R

Bastos, L

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Oral

Year

2026

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Filter results4 paper(s) found.
1. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton Canopies Cotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral
2. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-Making High-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enables...
3. Evaluation of Convolutional Neural Network Architectures for Stress Detection in Eucalyptus saligna Using Multispectral UAV Imagery Root malformation disorder (RMD) is a significant abiotic condition that impairs water and nutrient uptake in Eucalyptus saligna, leading to physiological stress and reduced stand uniformity. Early detection in commercial plantations is hampered by the extensive spatial scale and logistical limitations of manual field inspections. In this context, the present study evaluated the performance of three convolutional neural network (CNN) architectures, U-Net, U-Net++, and Attention U-Net, in detecting... L.D. Amaral, S.D. Pereira Da Silva, R.A. Fantinel, V. Richter, N. Download paper
4. 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

Filter results4 paper(s) found.

1. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton Canopies

Cotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral

2. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-Making

High-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enables...

3. Evaluation of Convolutional Neural Network Architectures for Stress Detection in Eucalyptus saligna Using Multispectral UAV Imagery

Root malformation disorder (RMD) is a significant abiotic condition that impairs water and nutrient uptake in Eucalyptus saligna, leading to physiological stress and reduced stand uniformity. Early detection in commercial plantations is hampered by the extensive spatial scale and logistical limitations of manual field inspections. In this context, the present study evaluated the performance of three convolutional neural network (CNN) architectures, U-Net, U-Net++, and Attention U-Net, in detecting... L.D. Amaral, S.D. Pereira Da Silva, R.A. Fantinel, V. Richter, N.

Download paper

4. 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