Robots in Agriculture: State of Art and Practical Experiences

The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming

Cooperation of unmanned systems for agricultural applications: A theoretical framework

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here

Cooperative heterogeneous robots for autonomous insects trap monitoring system in a precision agriculture scenario

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.The authors would like to thank the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). In addition, the authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. In addition, the authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Braganca (IPB) - Campus de Santa Apolonia, Portugal, Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Portugal, INESC Technology and Science - Porto, Portugal and Universidade de Trás-os-Montes e Alto Douro - Vila Real, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation used to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies

Алгоритмы расчета траекторий полета беспилотных воздушных судов для решения сельскохозяйственных задач

The relevance of using unmanned aerial vehicles (UAV) is substantiated in comparison with other methods of aerospace survey. The paper provides examples of tasks requiring the use of UAVs for aerial photography from different heights. It is shown that the introduction of agricultural robots, including UAVs, increases the speed of fi eld work, allows obtaining unique data necessary for the assessment of agricultural territories, crops processing and plant growth stimulation. It is noted that the problem of constructing the movement trajectories of a multirotor UAV for performing agricultural tasks within a minimum time limit remains unresolved. (Research purpose) To reduce the length of the trajectory covering a given area and reduce the flight time of a multirotor UAV, taking into account the analysis of possible obstacles and land plots that are beyond the task scope. (Materials and methods) Geometric methods have been used to calculate the UAV flight trajectory covering a given section, the trajectory of movement in an environment with obstacles to the designated point. Photogrammetry methods have been used for processing aerial photography images when forming an orthophotoplane and a terrain map. (Results and discussion) The trajectory calculated by the developed algorithm proves to meet all the requirements: it is continuous, has a minimum number of turns, it is smoothed, and feasible for a multirotor UAV. (Conclusions) It was determined that according to the proposed algorithm it takes less than 0.05 seconds to calculate the trajectory covering a rectangular section with the sides of 200 by 30 meters. It was found that the trajectory in the fi rst 10,200-square-meter section decreased by 9 percent, and in the second 950,000-square-meter section it reduced by 6 percent, compared with the length of the trajectory built using standard algorithms. The flight time reduced by 32 and 10 percent, respectively. The paper presents the key advantages of using UAV for video shooting such as: guaranteed high resolution of photographic materials and the ability to shoot at a given time, allowing for the crop condition assessment.Обосновали актуальность использования беспилотных воздушных судов (БВС) по сравнению с другими способами аэрокосмической съемки. Привели примеры задач, требующие применения БВС для аэрофотосъемки с разной высоты. Показали, что внедрение сельскохозяйственных роботов, в том числе БВС, ускоряет выполнение полевых работ, а также позволяет получать уникальные данные, необходимые для оценки сельскохозяйственных территорий, обработки посевов и стимуляции роста растений. Отметили, что проблема построения траекторий движения БВС мультироторного типа при выполнении сельскохозяйственных задач за минимальное время остается нерешенной. (Цель исследования) Уменьшить длину траектории покрытия заданного участка и сократить время полета БВС мультироторного типа с учетом анализа возможных препятствий и участков земли, не представляющих интереса при решении поставленной задачи. (Материалы и методы) Использовали геометрические методы расчета траектории полета БВС для покрытия заданного участка, траекторию движения в среде с препятствиями к назначенной точке. С помощью методов фотограмметрии провели обработку изображений аэрофотосъемки при формировании ортофотоплана и карты рельефа местности. (Результаты и обсуждение) Показали, что рассчитанная разработанным алгоритмом траектория удовлетворяет все требования: она непрерывна, имеет минимальное количество поворотов, сглажена, а также осуществима для БВС мультироторного типа. (Выводы) Определили, что для расчета по предложенному алгоритму траектории покрытия на прямоугольном участке со сторонами 200 и 30 метров потребовалось менее 0,05 секунды. Выявили, что траектория на первом участке, площадью 10 200 квадратных метров, уменьшилась на 9 процентов, а на втором, площадью 950 000 квадратных метров, – на 6 процентов, по сравнению с длиной траектории, построенной по стандартным алгоритмам, причем время полета сократилось на 32 и 10 процентов соответственно. Отметили основные преимущества применения БВС для видеосъемки: гарантированное высокое разрешение фотоматериалов и возможность съемки в заданное время, позволяющие оценивать состояние посевов

UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches

The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review

The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019)