Gabor Milics

Professor

Magyar Agrár- és Élettudományi Egyetem

Hungary

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About

Gabor Milics is a Professor at Magyar Agrár- és Élettudományi Egyetem in Hungary. His research focuses on precision agriculture, including variable rate seeding, satellite imagery application in crop monitoring, and UAV imagery analysis for yield prediction. Recent articles explore the impact of environmental conditions on insect pests, the calibration of low-cost soil moisture sensors, and methods for phosphorus extraction in agricultural practices.

Articles (10)

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2021

Collaborators (6)

Gergely Jakab

HUNGARY

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Mihaly Zalai

associate professor

Hungarian University of Agriculture and Life Sciences

HUNGARY

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Karl Behrendt

Professor of Agri-Tech Economic Modelling

Harper Adams University

UNITED KINGDOM

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Iona Yuelu Huang

Senior Lecturer

Harper Adams University

UNITED KINGDOM

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Zsolt Biró

associate professor

Magyar Agrár- és Élettudományi Egyetem

HUNGARY

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Csaba Centeri

Associate Professor

Magyar Agrár- és Élettudományi Egyetem

HUNGARY

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Gabor Milics

Research Interests

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About

Articles (10)

Variable Rate Seeding and Accuracy of Within-Field Hybrid Switching in Maize (<i>Zea mays</i> L.)

Monitoring Helicoverpa armigera Damage with PRISMA Hyperspectral Imagery: First Experience in Maize and Comparison with Sentinel-2 Imagery

Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (Helicoverpa armigera) Larvae

Comparing Different Phosphorus Extraction Methods: Effects of Influencing Parameters

Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

Impact of environmental conditions on the distribution of insect pests in Nitra region vineyards (Slovakia)

The effect of local samples in the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF) -based spectral prediction models

Lessons to be learned in adoption of autonomous equipment for field crops

Collaborators (6)

Gergely Jakab

Mihaly Zalai

Karl Behrendt

Iona Yuelu Huang

Zsolt Biró

Csaba Centeri

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