Proceedings :: International Society of Precision Agriculture

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László, M

Loganathan Girija, D

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Authors

László, M

Loganathan Girija, D

Usama Bin Sabir, S

Rathore, D

Khot, L.R

Mattupalli, C

Karkee, M

Loganathan Girija, D

Gorthi, S

Bhalekar, D

Khot, L.R

Keller, M

Szenek, Z

Szám, D

Centeri, C

Milics, G

László, M

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Agricultural Robotics, Automation, and Mechanization

Precision Horticulture and Specialty Crop Management

Digital Solutions for Soil Health, Water Quality, and Conservation Practices

Type

Oral

Poster

Year

2026

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Authors

Filter results4 paper(s) found.
1. UAV-Based Detection and Precision Management of Cirsium arvense: An End-to-End Workflow from Deep Learning to Variable-Rate Spraying Unmanned aerial vehicles (UAVs) combined with deep learning can enable site-specific weed management by transforming high-resolution imagery into actionable prescription maps for precision spraying. This study presents and validates an end-to-end operational workflow for detecting Cirsium arvense under real field conditions and converting detections into sprayer-compatible management zones. A 24.61 ha arable field in northwestern Hungary was surveyed using a multirotor UAV equipped with... M. László Download paper
2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee
3. Enhanced Deep Learning Framework Driven Grape Berry Temperature Estimation and 72-h Forecasting for Precision Heat Stress Management The increasing frequency of extreme summer heat events poses a significant threat to grape production in the Pacific Northwest (PNW), U.S., and globally. Elevated temperatures can induce sunburn, accelerate organic acid degradation, and cause anthocyanin loss, ultimately reducing berry quality. Berry surface temperature (BST), which can exceed ambient air temperature by up to 15 °C, is a primary indicator of heat stress severity. However, BST dynamics are governed by complex, nonlinear thermodynamic...
4. Quantitative Analysis of Sub-catchment Scale Erosion-sedimentation Dynamics in a Grassed Buffer Zone Using Hyper-resolution Soil Loss Modeling in a Hungarian Hilly Arable Environment Soil erosion represents one of the most severe environmental and economic risks in arable crop production, leading to the irreversible degradation of the topsoil layer. This research aims to provide a quantitative analysis of the sediment retention capacity of a grassed buffer strip installed within an erosion rill, utilizing 1*1 m resolution soil loss modeling. The methodological base of the study was the Unit Stream Power-based Erosion Deposition (USPED) model. The application of USPED... Z. Szenek, D. Szám, C. Centeri, G. Milics, M. László Download paper

Filter results4 paper(s) found.

1. UAV-Based Detection and Precision Management of Cirsium arvense: An End-to-End Workflow from Deep Learning to Variable-Rate Spraying

Unmanned aerial vehicles (UAVs) combined with deep learning can enable site-specific weed management by transforming high-resolution imagery into actionable prescription maps for precision spraying. This study presents and validates an end-to-end operational workflow for detecting Cirsium arvense under real field conditions and converting detections into sprayer-compatible management zones. A 24.61 ha arable field in northwestern Hungary was surveyed using a multirotor UAV equipped with... M. László

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2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers

High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee

3. Enhanced Deep Learning Framework Driven Grape Berry Temperature Estimation and 72-h Forecasting for Precision Heat Stress Management

The increasing frequency of extreme summer heat events poses a significant threat to grape production in the Pacific Northwest (PNW), U.S., and globally. Elevated temperatures can induce sunburn, accelerate organic acid degradation, and cause anthocyanin loss, ultimately reducing berry quality. Berry surface temperature (BST), which can exceed ambient air temperature by up to 15 °C, is a primary indicator of heat stress severity. However, BST dynamics are governed by complex, nonlinear thermodynamic...

4. Quantitative Analysis of Sub-catchment Scale Erosion-sedimentation Dynamics in a Grassed Buffer Zone Using Hyper-resolution Soil Loss Modeling in a Hungarian Hilly Arable Environment

Soil erosion represents one of the most severe environmental and economic risks in arable crop production, leading to the irreversible degradation of the topsoil layer. This research aims to provide a quantitative analysis of the sediment retention capacity of a grassed buffer strip installed within an erosion rill, utilizing 1*1 m resolution soil loss modeling. The methodological base of the study was the Unit Stream Power-based Erosion Deposition (USPED) model. The application of USPED... Z. Szenek, D. Szám, C. Centeri, G. Milics, M. László

Download paper