Peter Lootens

Orcid: 0000-0002-3275-3459

According to our database1, Peter Lootens authored at least 12 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Cross-domain transfer learning for weed segmentation and mapping in precision farming using ground and UAV images.
Expert Syst. Appl., 2024

2023
Predicting yield of individual field-grown rapeseed plants from rosette-stage leaf gene expression.
PLoS Comput. Biol., 2023

2022
Multispectral UAV-Based Monitoring of Leek Dry-Biomass and Nitrogen Uptake across Multiple Sites and Growing Seasons.
Remote. Sens., December, 2022

Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming.
CoRR, 2022

2021
Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses.
Remote. Sens., 2021

Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning.
Remote. Sens., 2021

Data collection design for calibration of crop models using practical identifiability analysis.
Comput. Electron. Agric., 2021

2020
Gloxinia - An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants.
Sensors, 2020

Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials.
Remote. Sens., 2020

In-field detection of <i>Alternaria solani</i> in potato crops using hyperspectral imaging.
Comput. Electron. Agric., 2020

Limitations of snapshot hyperspectral cameras to monitor plant response dynamics in stress-free conditions.
Comput. Electron. Agric., 2020

2018
Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery.
Int. J. Appl. Earth Obs. Geoinformation, 2018


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