Heinz Bernhardt

Professor

Ludwig-Maximilians-Universität München

Germany

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Professor Heinz Bernhardt is a faculty member at Technische Universität München, Germany. His research primarily focuses on the application of advanced technologies in agriculture, including deep learning for weed instance segmentation, mechanical-chemical weed control methods, and the usability of IoT-infused digital services in farming. He has contributed significantly to studies on energy management systems and digital agriecosystems.

Heinz Bernhardt

Research Interests

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Articles (18)

Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning

Combined Mechanical–Chemical Weed Control Methods in Post-Emergence Strategy Result in High Weed Control Efficacy in Sugar Beet

Influence of Temperature and LED Light Spectra on Flavonoid Contents in Poa pratensis

Usability Testing of Novel IoT-Infused Digital Services on Farm Equipment Reveals Farmer’s Requirements towards Future Human–Machine Interface Design Guidelines

Standardized Decision-Making for the Selection of Calf and Heifer Rearing Using a Digital Evaluation System

Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”

How to Successfully Orchestrate Content for Digital Agriecosystems

Mind the Market Opportunity: Digital Energy Management Services for German Dairy Farmers

Potential and Improvement of Maintenance Efficiency of Agricultural PTO Shafts by a New Digital Maintenance Assistant

Requirements and Economic Implications of Integrating a PV-Plant-Based Energy System in the Dairy Production Process

Sistemas de Cogeneración: Aplicaciones y posible obsolescencia frente a otras alternativas

Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study

Three Methods of Site-Specific Yield Mapping as a Data Source for the Delineation of Management Zones in Winter Wheat

Analysis of Nitrogen Uptake in Winter Wheat Using Sensor and Satellite Data for Site-Specific Fertilization

Comparison of a Conventional Harvesting Technique in Alfalfa and Red Clover with a Leaf Stripping Technique Regarding Dry Matter Yield, Total Leaf Mass, Leaf Portion, Crude Protein and Amino Acid Contents

Evaluation of LiDAR for the Free Navigation in Agriculture

System Design and Validation of a Wireless Sensor Monitoring System in Silage

Digital Agriculture Infrastructure in the USA and Germany

Collaborators (3)

Kang Yu

Chenghao Lu

Wolfgang Büscher

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