A5000548850- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Soil Mechanics and Vehicle Dynamics
- Modular Robots and Swarm Intelligence
- Crop Yield and Soil Fertility
- Food Supply Chain Traceability
- Robotic Path Planning Algorithms
- Robotics and Sensor-Based Localization
- Greenhouse Technology and Climate Control
- Spectroscopy and Chemometric Analyses
- Weed Control and Herbicide Applications
- Plant Surface Properties and Treatments
- Soil Geostatistics and Mapping
- Soil Carbon and Nitrogen Dynamics
- Robotics and Automated Systems
- 3D Surveying and Cultural Heritage
- Biological Control of Invasive Species
- Nematode management and characterization studies
- Manufacturing Process and Optimization
- Wastewater Treatment and Nitrogen Removal
- Agricultural Engineering and Mechanization
- Animal Behavior and Welfare Studies
- Plant Pathogens and Resistance
Agro Business Park
2021-2022
Aarhus University
2007-2021
Signal Processing (United States)
2014-2017
University of Southern Denmark
2008-2016
Maersk (Denmark)
2015-2016
University of Bergen
2013
Institutul National Victor Babes
2013
Hospital Curry Cabral
2013
Hospital de León
2013
University Hospital Dubrava
2013
Summary Site‐specific weed control technologies are defined as machinery or equipment embedded with that detect weeds growing in a crop and, taking into account predefined factors such economics, take action to maximise the chances of successfully controlling them. In this study, we describe basic parts site‐specific technologies, comprising sensing systems, management models and precision implements. A review state‐of‐the‐art shows several systems implements have been developed over last...
A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for estimation of crop biomass agriculture. The work presented this paper contributes to sensory UAV setup design mapping textual analysis agricultural fields. LiDAR data are combined with from Global Navigation Satellite System (GNSS) Inertial Measurement Unit (IMU) sensors conduct proposed method facilitates recordings...
In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds machinery. Detection recognition wildlife within fields is important reduce mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes automated detection classification thermal imaging. methods results based on top-view images taken manually from a lift motivate towards unmanned aerial vehicle-based recognition....
Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms trained classifying a predefined set of types. These have difficulties distant heavily occluded objects are, by definition, not capable unknown types or unusual scenarios. The visual characteristics an agriculture field is homogeneous, obstacles, like people, animals other occur rarely...
A database of images approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB with a physical resolution roughly 10 pixels per mm. To standardise the evaluation classification results obtained database, benchmark based on $f_{1}$ scores proposed. The dataset available https://vision.eng.au.dk/plant-seedlings-dataset
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) in situ images covering 18 or families. Images weeds growing within variety crops were gathered across variable environmental conditions with regards to soil types, resolution light settings. Then, 9649 these used for training the computer, which divided into nine classes. The performance this proposed convolutional neural network approach was evaluated...
In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. sensors are capable imaging Earth’s surface independently weather conditions, local time day, penetrating waves through clouds, containing spatial information on agricultural crop types. Based these characteristics, main goal sought this research is to reveal SAR capability recognizing various crops growth season a more clarified detailed way by...
In this paper, an algorithm for obstacle detection in agricultural fields is presented. The based on existing deep convolutional neural net, which fine-tuned of a specific obstacle. ISO/DIS 18497, emerging standard safety highly automated machinery agriculture, barrel-shaped defined as the should be robustly detected to comply with standard. We show that our net capable detecting precision 99 . 9 % row crops and 90 8 grass mowing, while simultaneously not people other very distinct obstacles...
GrassClover is a diverse image and biomass dataset collected in an outdoor agricultural setting. The images contain dense populations of grass clover mixtures with heavy occlusions occurrences weeds. Fertilization treatment mixed crops depend on the local species composition. Therefore, overall challenge related to predicting composition canopy biomass. three different acquisition systems ground sampling distances 4-8 px/mm. observed vary both setting (field vs plot trial), seed...
For decades, significant effort has been put into the development of plant detection and classification algorithms. However, it difficult to compare performance different algorithms, due lack a common testbed, such as public available annotated reference dataset. In this paper, we present Open Plant Phenotype Database (OPPD), dataset for classification. The contains 7590 RGB images 47 species. Each species is cultivated under three growth conditions, provide high degree diversity in terms...
This study explores the impact of climatic variability on generalization capabilities a deep learning model for pixel-level crop classification using multi-temporal Sentinel-1 SAR data in Denmark. With agriculture accounting 61% Denmark’s land area, accurate and timely mapping is essential providing insights into distribution, offering valuable information to advisors authorities support large-scale agricultural management, address challenges posed by changing conditions.Our...
Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared current crop production methods become an increasingly active area of research. Tractor guidance systems for supervised navigation implement control have reached market, prototypes field robots performing tasks without human intervention also exist. But research advanced cognitive perception behaviour that is required enable a more efficient, reliable safe autonomy becomes...
In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The comprises approximately 2 h of raw sensor data from tractor-mounted system grass mowing scenario Denmark, October 2016. Sensing modalities include stereo camera, thermal web 360 ∘ LiDAR and radar, while precise localization is available fused IMU GNSS. Both static moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles vegetation. All have ground truth...
Optimal fertilization of clover-grass fields relies on knowledge the clover and grass fractions. This study shows how can be obtained by analyzing images collected in automatically. A fully convolutional neural network was trained to create a pixel-wise classification clover, grass, weeds red, green, blue (RGB) mixtures. The estimated fractions dry matter from were found highly correlated with real matter, making this cheap non-destructive way monitoring fields. solely simulated top-down...
Abstract The adoption of site‐specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success real‐time identification, phenotyping and accurate mapping using various sensors platforms, integration SSWM tools into protocols limited. This gap was therefore a central topic discussion at most recent workshop Working Group arranged European Weed Research Society (EWRS). insight paper...
Weeding operations represent an effective approach to increase crop yields. Reliable and precise weed detection is a prerequisite for achieving high-precision monitoring control in precision agriculture. To develop detecting weeds within the red, green, blue (RGB) images, two state-of-the-art object models, EfficientDet (coefficient 3) YOLOv5m, were trained on more than 26,000 situ labeled images with monocot/dicot classes recorded from 200 different fields Denmark. The dataset was collected...