Rasmus Nyholm Jørgensen

Rasmus Nyholm Jørgensen
Aarhus University | AU · Department of Engineering

Ph.D. Cand.Agro

About

166
Publications
54,211
Reads
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2,555
Citations
Citations since 2017
43 Research Items
1799 Citations
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Additional affiliations
February 2021 - present
AgroIntelli
Position
  • Senior Researcher
Description
  • Utilization of the agricultural autonomous field robot, Robotti, within large plot trials with special empathy on Computer Vision and Machine Learning
January 2012 - January 2021
Aarhus University
Position
  • Senior Researcher
January 2009 - December 2011
University of Southern Denmark

Publications

Publications (166)
Article
Full-text available
Weeding operations represent an effective approach to increase crop yields. Reliable and precise weed detection is a prerequisite for achieving high-precision weed monitoring and control in precision agriculture. To develop an effective approach for detecting weeds within the red, green, and blue (RGB) images, two state-of-the-art object detection...
Preprint
Full-text available
Information and communications technology (ICT) within the agricultural sector is characterized by a widespread use of proprietary data formats, a strong lack of interoperability standards, and a tight connection to specific hardware implementations resulting from vendor lock-in. This partly explains why ICT has not yet had its full impact within t...
Book
Full-text available
Many endeavours in precision agriculture use some kind of sensor to gain relatively inexpensive information on the spatial and temporal variation in crops, soil, weeds, diseases, and so on. However, information about sensors is scattered throughout the literature. This text fills an important niche by bringing together information on a wide range o...
Chapter
Conventional weed control methods are based on uniform treatments of the whole field, however, weeds are not distributed uniformly within fields, which means that the uniform distribution of herbicides is inappropriate. Considerable research has been conducted on different aspects of site-specific weed management in the past three decades from fund...
Article
Full-text available
In agriculture, explainable deep neural networks (DNNs) can be used to pinpoint the discriminative part of weeds for an imagery classification task, albeit at a low resolution, to control the weed population. This paper proposes the use of a multi-layer attention procedure based on a transformer combined with a fusion rule to present an interpretat...
Article
Full-text available
The targeted treatment of weeds is an expanding part of precision farming in many countries. Targeted weed treatments, using precision spray maps, reduce herbicide consumption, whilst still maintaining long term weed control. Assembling accurate spray maps is a vital part of this process. However, acceptable accuracy in spray maps is difficult to q...
Article
Estimating the plant population after the emergence of plants seems to be the most accurate way to make a fair judgment about the quality of the sowing across a field. A pixel-level classification model based on deep learning methods was used to predict each pixel in the image to recognise the location and population of plants. A U-Net based model...
Article
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 in real‐time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed...
Article
Full-text available
Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation...
Article
Full-text available
One of the methods of assessing the performance of seed drills may be to compare the performance with the crop population growth per field surface unit. Pixels of crop emergence zone appear to have similar characteristics concerning image parameter variations between soil and crop. Using deep learning methods based on convolution neural networks to...
Conference Paper
Satellite imagery contains valuable large-scale information for precision farming. However, the low-resolution of satellite images can make it challenging to extract crop status information due to mixed pixels, in particular within multi-species crop stands like grass-clover for silage. In contrast, proximal high-resolution images with centimeter t...
Article
Full-text available
For decades, significant effort has been put into the development of plant detection and classification algorithms. However, it has been difficult to compare the performance of the different algorithms, due to the lack of a common testbed, such as a public available annotated reference dataset. In this paper, we present the Open Plant Phenotype Dat...
Article
Full-text available
Lack of annotated data for training of deep learning systems is a challenge for many visual recognition tasks. This is especially true for domain-specific applications, such as plant detection and recognition, where the annotation process can be both time-consuming and error-prone. Generative models can be used to alleviate this issue by producing...
Article
Plants seedlings are a part of a domain with low inter-class and relatively high intra-class variance with respect to visual appearance. This paper presents an approach for generating artificial image samples of plant seedlings using generative adversarial networks (GAN) to alleviate for the lack of training data for deep learning systems in this d...
Article
Full-text available
ESA provides Sentinel-1 synthetic aperture radar satellite data freely for research and industry. Sentinel-1 data have shown the potential for remotely monitoring conditions in individual agricultural fields on a weekly basis. Researchers have access to the same Sentinel-1 dataset, so independent validation should be possible. Well documented studi...
Article
Full-text available
In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. Radar sensors are capable of imaging Earth’s surface independently of the weather conditions, local time of day, penetrating of waves through clouds, and containing spatial information on agricultural crop...
Preprint
Full-text available
ESA operates the Sentinel-1 satellites, which provides Synthetic Aperture Radar (SAR) data of Earth. Recorded Sentinel-1 data have shown a potential for remotely observing and monitoring local conditions on broad acre fields. Remote sensing using Sentinel-1 have the potential to provide daily updates on the current conditions in the individual fiel...
Conference Paper
Full-text available
This conference on biosystems engineering wants agriculture to add a valued contribution to a modern society of the 21th century. We will therefore host engineers, scientists, industry people and practitioners to share and integrate knowledge and expertise from various disciplines. They will highlight innovative technological methods and novel syst...
Conference Paper
Full-text available
Information about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying. For detecting weeds, this study proposes a fully convolutional neural network, which det...
Article
Full-text available
Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the...
Article
Full-text available
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light...
Preprint
Full-text available
Mapping field environments into point clouds using a 3D LIDAR has the ability to become a new approach for online estimation of crop biomass in the field. The estimation of crop biomass in agriculture is expected to be closely correlated to canopy heights. The work presented in this paper contributes to the mapping and textual analysis of agricultu...
Article
Full-text available
Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatica...
Article
Full-text available
This paper describes a collaborative modelling approach to automated and robotic agricultural vehicle design. The Cresendo technology allows engineers from different disciplines to collaborate and produce system models. The combined models are called co-models and their execution co-simulation. To support future development efforts a template libra...
Article
Full-text available
Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) ima...
Article
Full-text available
The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary results of an image analysis method for non-destructively estimating the...
Article
Full-text available
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 the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. L...
Article
Full-text available
A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per mm. To standardise the evaluation of classification results obtained with the database, a benchmark based on $f_{1}$ scores is prop...
Article
Full-text available
In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and rada...
Conference Paper
Full-text available
Creeping thistle is a perennial weed that tends to grow in patches and cause significant yield loss in cereal crops. A tool for detecting thistles, Thistle Tool, have been developed by Rasmussen et al. The tool detects the level of green in images and compares it with a threshold. The images are taken by a drone in altitudes of 10 m and 50 m, divid...
Article
In order to exploit potentials of 20–40% reduction of herbicide use, as documented by use of Decision Support Systems (DSS), where requirements for manual field inspection constitute a major obstacle, large numbers of digital pictures of weed infestations have been collected and analysed manually by crop advisors. Results were transferred to: 1) DS...
Article
Full-text available
This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on a...
Article
Full-text available
This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Senti...
Article
Full-text available
The concept of autonomous farming concerns automatic agricultural machines operating safely and efficiently without human intervention. In order to ensure safe autonomous operation, real-time risk detection and avoidance must be undertaken. This paper presents a flexible vehicle-mounted sensor system for recording positional and imaging data with a...
Article
Full-text available
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 are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heav...
Article
Full-text available
The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studyin...
Conference Paper
This paper proposes an algorithm which uses the depth information acquired from an active sensor as guidance for a block matching stereo algorithm. In the proposed implementation, the disparity search interval used for the block matching is reduced around the depth values obtained from the active sensor, which leads to an improved matching quality...
Conference Paper
Drone technology represents a significant potential in precision farming. Most applications, however, require accurate positioning of the drone. The aim of this work is to perform accurate low altitude contour flight above an agricultural field using a low cost multirotor drone and the AutoQuad 1 flight controller. We hypothesize that at wind veloc...
Conference Paper
Full-text available
Effective weed control, using either mechanical or chemical means, relies on knowledge of the crop and weed plant occurrences in the field. This knowledge can be obtained automatically by analyzing images collected in the field. Many existing methods for plant detection in images make the assumption that plant foliage does not overlap. This assumpt...
Conference Paper
In recent years, mapping and automation has been increasingly investigated and applied in precision agriculture. The ultimate goal of this development is to apply autonomous vehicles operating efficiently without any human intervention. Such autonomous operation imposes severe safety hazards, demanding accurate and robust risk detection, and avoida...
Article
Full-text available
In this paper, an algorithm for obstacle detection in agricultural fields is presented. The algorithm is based on an existing deep convolutional neural net, which is fine-tuned for detection of a specific obstacle. In ISO/DIS 18497, which is an emerging standard for safety of highly automated machinery in agriculture, a barrel-shaped obstacle is de...
Article
Knowledge of the precise position of crop plants is a prerequisite for effective mechanical weed control in robotic weeding application such as in crops like sugar beets which are sensitive to mechanical stress. Visual detection and recognition of crop plants based on their shapes has been described many times in the literature. In this paper the p...
Article
Full-text available
Rapid robotic system development has created a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present a collaborative modeling technique that combines discrete-event models of controller software with continuous-time models of physical robot components. The proposed co-modeling method u...
Conference Paper
Full-text available
Autonomous navigation and operation of agricultural vehicles is a challenging task due to the rather unstructured environment. An uneven terrain consisting of ground and vegetation combined with the risk of non-traversable obstacles necessitates a strong focus on safety and reliability. This paper presents an object detection and terrain classifica...
Chapter
Full-text available
Catch crops are established after harvest of the main crop to reduce nitrogen (N) leaching to groundwater. The purpose of predicting dry matter and N-uptake is to investigate the potential for optimizing variable N rate application in the spring. Secondly, the purpose is to evaluate the use of a catch crop to take up N in the autumn to lower the ri...
Article
Segmentation is a popular preprocessing stage in the field of machine vision. In agricultural applications it can be used to distinguish between living plant material and soil in images. The normalized difference vegetation index (NDVI) and excess green (ExG) color features are often used in the segmentation of images with multiple color channels....
Article
Full-text available
In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this...
Article
Full-text available
Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods and has become an increasingly active area of research. Tractor guidance systems for supervised navigation and implement control have reached the market, and prototypes of field robots performing pre...
Article
Non-destructive assessment of herbicide effects may be able to support integrated weed management. To test whether effects of herbicides on canopy variables could be detected by sensors, two crops were used as models and treated with herbicides at BBCH 20 using a logarithmic sprayer. Twelve days after spraying at BBCH 25 and 42 days after sowing, n...
Conference Paper
Full-text available
Precision weeding is one of the most promising applications for autonomous service robots in biological production. Herbicides have been the default weeding solution during the past decades, but there is a growing concern about the environmental impact on drinking water reservoirs etc. The use of computer vision and precision spraying technology ma...
Conference Paper
Full-text available
Load-carrying agricultural vehicles can experience load changes during operation. The change in load is present in operational tasks where animal food is dispensed, sprayer tanks are emptied or operational implements change position over time. The change in load is influencing the weight distribution of the vehicle and consequently the steering and...