19th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2022), 14 - 16 July, 2022. Lisbon - Portugal., This paper presents a general procedure for enabling autonomous row following in crops during early-stage
growth, without relying on absolute localization systems. A model based on deep learning techniques (object
detection for wide-row crops and segmentation for narrow-row crops) was applied to accurately detect both
types of crops. Tests were performed using a manually operated mobile platform equipped with an RGB and
a time-of-flight (ToF) cameras. Data were acquired during different time periods and weather conditions, in
maize and wheat fields. The results showed the success on crop detection and enables the future development
of a fully autonomous navigation system in cultivated fields during early stage of crop growth., This article is part of a the WeLASER project that has received funding from the European Union’s Horizon
2020 research and innovation programme under grant agreement No 101000256, Peer reviewed

Proceedings of the 10th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2022). Information and Communication Technologies in Agriculture, Food and Environment. 22-25 September, 2022. Athens, Greece., In recent years, digitization has created a significant impact on food production systems, allowing various technologies and advanced data processing strategies to be implemented. Alongside the introduction of tools for the digitalization of the field, the automation of tasks through the use of mobile robots has also been growing in the last decades. These systems are nourished by the acquisition of field data to carry out autonomous operations including weed management, application of fertilizers, and harvesting, among others. One of the current challenges of integrating robotic solutions in the digital age of agriculture is the development of scalable and interoperable systems, able to manage data obtained from third parties. This work presents an overall methodology for map creation by using open-source tools, which allows data in the daily activities of an agricultural farm to be managed, and autonomous tasks of robotic systems to be planned and supervised., This article is part of the WeLASER project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000256., Peer reviewed

Special Issue "Robots and Autonomous Machines for Agriculture Production", Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet., This article is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101000256., Peer reviewed

This study presents the development of a route planner, called Mission Planner, for an
agricultural weeding robot that generates efficient and safe routes both in the field and on the farm
using a graph-based approach. This planner optimizes the robot’s motion throughout the farm
and performs weed management tasks tailored for high-power laser devices in narrow-row crops
(wheat, barley, etc.) and wide-row crops (sugar beet, maize, etc.). Three main algorithms were
integrated: Dijkstra’s algorithm to find the most optimal route on the farm, the VRMP (Visibility
Road-Map Planner) method to select the route within cultivated fields when roads are not visible,
and an improved version of the Hamiltonian path to find the best route between the crop lines. The
results support the effectiveness of the strategies implemented, demonstrating that a robot can safely
and efficiently navigate through the entire farm and perform an agricultural treatment, in this case
study, in laser-based weed management. In addition, it was found that the route planner reduced the
robot’s operation time, thus improving the overall efficiency of precision agriculture., This article is part of a project that has received funding from the European Union’s
Horizon 2020 research and innovation program under grant agreement No 101000256., Peer reviewed

This study introduces two methods for crop identification and growth stage determination, focused primarily on enabling mobile robot navigation. These methods include a two-phase approach involving separate models for crop and growth stage identification and a one-phase method employing a single model capable of handling all crops and growth stages. The methods were validated with maize and sugar beet field images, demonstrating the effectiveness of both approaches. The one-phase approach proved to be advantageous for scenarios with a limited variety of crops, allowing, with a single model, to recognize both the type and growth state of the crop and showed an overall Mean Average Precision (mAP) of about 67.50%. Moreover, the two-phase method recognized the crop type first, achieving an overall mAP of about 74.2%, with maize detection performing exceptionally well at 77.6%. However, when it came to identifying the specific maize growth state, the mAP was only able to reach 61.3% due to some difficulties arising when accurately categorizing maize growth stages with six and eight leaves. On the other hand, the two-phase approach has been proven to be more flexible and scalable, making it a better choice for systems accommodating a wide range of crops, This article is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000256., Peer reviewed

This article belongs to the Special Issue Robotics and AI for Precision Agriculture., Mobile robots have become increasingly important across various sectors and are now essential in agriculture due to their ability to navigate effectively and precisely in crop fields. Navigation involves the integration of several technologies, including robotics, control theory, computer vision, and artificial intelligence, among others. Challenges in robot navigation, particularly in agriculture, include mapping, localization, path planning, obstacle detection, and guiding control. Accurate mapping, localization, and obstacle detection are crucial for efficient navigation, while guiding the robotic system is essential to execute tasks accurately and for the safety of crops and the robot itself. Therefore, this study introduces a Guiding Manager for autonomous mobile robots specialized for laser-based weeding tools in agriculture. The focus is on the robot’s tracking, which combines a lateral controller, a spiral controller, and a linear speed controller to adjust to the different types of trajectories that are commonly followed in agricultural environments, such as straight lines and curves. The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform with a width of about 1.48 m, in complex and varying field conditions including loose soil, stones, and humidity. The lateral controller presented an average absolute lateral error of approximately 0.076 m and an angular error of about 0.0418 rad, while the spiral controller presented an average absolute lateral error of about 0.12 m and an angular error of about 0.0103 rad, with a horizontal accuracy of about ±0.015 m and an angular accuracy of about ±0.009 rad, demonstrating its effectiveness in real farm tests., This article is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000256, Peer reviewed

Purpose: Challenges in sustainable development envisioned in the European Union for the agricultural sector require innovation to raise the efficiency of production and safety of farming processes for farmers and ensure food safety for consumers. One of the key productivity factors in plant production is effective weeding. The WeLASER project aimed to develop a high-power autonomous vehicle with lasers to control weeds. To be sustainable, the invention should have a high environmental performance in the whole life cycle perspective, including its production, use in agriculture, and end-of-life phase. In the publication, a life cycle assessment (LCA) of the WeLASER weeding robot is presented. The aim was to identify weak and strong aspects of the invention in environmental terms and provide suggestions for its improvement. Methods: The machinery was characterized based on technical data provided by the developers, relevant literature, Ecoinvent 3.8 database, and own calculations. The quantitative assessment of environmental impacts was performed using the Simapro tool. For interpretation Recipe 2016 method (egalitarian perspective) was applied. Results: The results show that the energy issue related to autonomous laser-based weeding machine operations is the most challenging. It is related to impacts on climate change indicators and fossil fuel depletion. Production phase is characterized with impacts on human toxicity and is related to extensive application of electronic and electric components in the robot. Conclusion: In comparison with other weeding techniques, the high-power autonomous vehicle with lasers to control weeds shows potential for environmentally efficient use of the machinery in practice. Achieving high performance in the life cycle perspective requires improvements in the design, operational features, and smart application in agricultural practice enhanced through expertise, guidance, and advice., Open access funding provided by Copenhagen University We are thankful for the funding of the project WeLASER (Sustainable Weed Management in Agriculture with Laser-based Autonomous Tools, Grant Agreement ID 101000256) by European Commission under H2020-EU.3.2.1.1. Further, all sources of this paper have been cited and adequately referenced., Peer reviewed

Purpose: Precision agriculture, a key driver in the future development of agriculture, offers numerous ecological, social, and economic benefits, including innovative solutions for weed control – a major factor affecting farm yields. Traditional weed control methods, such as chemical and mechanical weeding, face limitations due to environmental impacts. Autonomous weed control technologies using high-power lasers represent a promising technical solution, yet their practical implementation raises societal challenges regarding cost distribution among farmers, producers, and society. Methods: A case study employing the life cycle costing (LCC) method on a high-power thulium laser weeder provides insight into these technologies’ future. This study includes an environmental LCC analysis to assess the devices’ practical application and their environmental and economic performance. Results: The results show that the external environmental costs are minimal compared to substantial internal costs, primarily from energy use (e.g. diesel), and account for 4–5% of total costs. However, these costs could be reduced or further internalised through policy measures to promote renewable energy and technical innovation in agriculture. The cost structure is influenced by the machinery designs and application scenarios, whereby the acquisition and fuel costs are decisive. The sensitivity analysis highlighted that weed density is crucial for estimating operating costs. Conclusions: Finally, laser-based methods could be considered as part of Integrated Weed Management (IWM), which can maximise weed control performance when used strategically. Optimal conditions for efficient WeLASER utilisation exist in well-managed fields with low weed density. Additionally, integrating WeLASER with other tools, such as mechanical weed removal systems for rows and inter-rows, could enhance its versatility and cost-effectiveness., We are thankful for the funding of the project WeLASER (Sustainable Weed Management in Agriculture with Laserbased Autonomous Tools, Grant Agreement ID 101000256) by European Commission under H2020-EU.3.2.1.1., Peer reviewed