Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Authors
El Houssein Chouaib HarikAbstract
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Authors
El Houssein Chouaib HarikAbstract
In this paper, we investigated the idea of including mobile robots as complementary machinery to tractors in an agricultural context. The main idea is not to replace the human farmer, but to augment his/her capabilities by deploying mobile robots as assistants in field operations. The scheme is based on a leader–follower approach. The manned tractor is used as a leader, which will be taken as a reference point for a follower. The follower then takes the position of the leader as a target, and follows it in an autonomous manner. This will allow the farmer to multiply the working width by the number of mobile robots deployed during field operations. In this paper, we present a detailed description of the system, the theoretical aspect that allows the robot to autonomously follow the tractor, in addition to the different experimental steps that allowed us to test the system in the field to assess the robustness of the proposed scheme.
Authors
Shelemia Nyamuryekung'e Andrew Cox Andres Perea Richard Estell Andres F. Cibils John P. Holland Tony Waterhouse Glenn Duff Micah Funk Skye Aney Matthew M. McIntosh Sheri Spiegal Brandon Bestelmeyer Santiago A. UtsumiAbstract
Virtual fencing (VF) is an alternative method to control livestock dispersal. This method consists of the use of animal wearable collars that employ auditory-electric pulse cues to deter animals from exiting their predefined containment zones. The study aimed to document skin defense (SkinM) and association learning mechanism (AssocM) in describing the conditioning behavior of the VF application. Nursing Brangus cows at the New Mexico State University’s Chihuahuan Desert Rangeland Research Center were allotted three days of free access to feeding areas (0.19ha) with VF-deactivated (VF-Off) or VF-Activated (VF-On) collars restricting one-third of the penned area. This training sequence was repeated twice (6-day/Period) with two replications (n=11 and 17cows). The VF collars communicated real-time animal positions at 15-minute intervals. ANOVA was used to compare daily-derived variables per cattle on the percentage of time spent within the containment and restricted zones (SkinM) and the number of auditory and electric pulses emitted during the VF-On configurations (AssocM). The VF-On treatment increased the percentage of time collared animals spent within the containment zone (98.4 vs.72.0 ±1.0 %Time;P<0.01) and reduced the percentage of time within the restricted zone (1.6 vs.28.0 ±1.0 %Time;P<0.01) compared to the VF-Off treatment. Exposure to VF-On in Period 1 triggered a greater frequency of auditory (1.8 vs.0.6 ±0.4;P<0.01) and electrical pulses (0.7 vs.0.2 ±0.2;P<0.01) than in Period 2. Results indicate that groups of cows learn rapidly to respond to VF boundaries by reducing the time spent within the restricted areas (SkinM) and relying increasingly on auditory cues to alter behavior (AssocM).
Authors
Anupam Gogoi Bikal Ghimire Erik Lysøe Mandeep Poudel Håvard Eikemo Jahn Davik Arne Stensvand May Bente BrurbergAbstract
No abstract has been registered
Authors
Claire CoutrisAbstract
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Abstract
Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.
Editors
Giuseppe E. Scarascia-Mugnozza Vicente Guallart Fabio Salbitano Giovanna Ottaviani Aalmo Stefano BoeriAbstract
This edited volume centers around the concept of BioCities, which aim to unify nature and urban spaces in order to reverse the effects of global climate change and inequity. Following this principle, the authors propose multiple approaches for sustainable city growth. The discussed concepts are not only relevant for newly constructed cities, but offer transformative perspectives for existing settlements as well. Placing nature at the forefront of city planning is not an entirely new concept, so the authors build on established ideas like the garden city, green city, eco-city, or smart city. All chapters aim to highlight aspects to develop a city that is a resilient nature-based socio-ecological system. Many of these concepts were formed in an effort to copy the best traits of a forest ecosystem: a home for many different species that build complex communities. Much like many of our forests, urban areas are managed by humans for multifunctional purposes, using living and abiotic components. This viewpoint helps to understand the potential and limitations of sustainable growth. With these chapters, the authors want to inspire planners, ecologists, urban foresters and decision makers of the future.
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Abstract
No abstract has been registered