Reducción de contaminantes atomsféricos e hídricos en agricultura de precisiónutilizando sistemas robotizados

[EN]In the last decades there has been a large increase in environmental pollution. The incessant use of fossil fuels generates large air pollution with consequent climate change, in addition to the health problems caused by these pollutant emissions. These fuels are the main energy source for mobile vehicles, such as agricultural ones. Another problem generated in the current intensive agriculture is the use of chemicals to combat undesired pests that undermine and damage the production. Many of these products usually arrive to the water table polluting groundwater. Considering these issues, this doctoral dissertation presents a series of research publications to reduce pollution in agricultural tasks using automated systems. Concretely, the 4 publications presented in this doctoral dissertation by compendium of publications are focused on reducing atmospheric and water pollutants using robotic systems for precision treatments in agriculture. For the experimental tests presented in these publications, we have used robotic vehicles and implements developed in the RHEA project (European Union FP7-NMP 245986), the agricultural tasks considered in these publications have also been those developed within this project: (a) weed control in agricultural crops using herbicides; (b) weed control in crops with wide row spaces that can withstand high temperatures over short periods of times (such as corn, onions, garlic, leeks, etc.) by direct application of flame; and (c) pest control in trees using insecticides.[ES] En las últimas décadas se ha producido un gran aumento en la contaminación del medioambiente. El incesante uso de combustible fósil genera una gran contaminación atmosférica con las consecuentes alteraciones climáticas, además de los problemas de salud provocados por estas emisiones contaminantes. Estos combustibles son la principal fuente de energía para vehículos móviles, como lo son los vehículos agrícolas. Otra problemática generada en la actual agricultura intensiva es el uso de productos químicos utilizados para combatir las plagas indeseadas que merman y dañan la producción. Gran parte de estos productos suele terminar en el subsuelo contaminando las aguas freáticas. Abordando estas problemáticas, esta memoria de tesis doctoral presenta una serie de publicaciones de investigación para reducir la contaminación generada en las tareas agrícolas llevadas a cabo por sistemas automatizados. Concretamente, las 4 publicaciones expuestas en esta memoria de tesis doctoral por compendio de publicaciones se centran en la reducción contaminantes atmosféricos e hídricos utilizando sistemas robotizados para tratamientos de precisión aplicados en agricultura. Para las pruebas experimentales presentadas en estas publicaciones se han utilizado los vehículos robóticos e implementos desarrollados en el proyecto RHEA (European Union FP7-NMP 245986), las tareas agrícolas consideradas en estas publicaciones también han sido las desarrolladas dentro de este proyecto: (a) control de malas hierbas en cultivos agrícolas utilizando herbicidas; (b) control de malas hierbas en cultivos con surcos amplios y gran resistencia a temperaturas elevadas durante pequeños periodos de tiempo (como maíz, cebollas, ajos, puerros, etc.) mediante la aplicación directa de llamas; y (c) control de plagas en árboles aplicando insecticidas. Además, es importante tener en cuenta que gran parte de los resultados obtenidos se pueden extender a otras tareas, tanto del sector agrícola como de otros sectores.La investigación llevada a cabo para obtener los resultados de las publicaciones expuestas en esta memoria de tesis doctoral (presentada en modalidad de compendio de publicaciones) ha recibido financiación del Séptimo Programa Marco de la Unión Europea [FP7 / 2007-2013] en virtud de Acuerdo de Subvención nº 245986.Peer reviewe

Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

Sensor architecture and task classification for agricultural vehicles and environments

[EN] The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical wayThe research activities devoted to the study of sensor and system architectures for agricultural intelligent vehicles carried out during 2010 have been supported by the Spanish Ministry of Science and Innovation through Project AGL2009-11731.Rovira Más, F. (2010). Sensor architecture and task classification for agricultural vehicles and environments. Sensors. 10(12):11226-11247. https://doi.org/10.3390/s101211226S1122611247101

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

GNSS in Precision Agricultural Operations

Today, there are two Global Navigation Satellite Systems (GNSS) that are fully operational and commercially available to provide all-weather guidance virtually 24 h a day anywhere on the surface of the earth. GNSS are the collection of localization systems that use satellites to know the location of a user receiver in a global (Earth-centered) coordinate system and this has become the positioning system of choice for precision agriculture technologies. At present North American Positioning System known as Navigation by Satellite Timing and Ranging Global Position System (NAVSTAR GPS or simply GPS) and Russian Positioning System known as Globalnaya Navigatsionnaya Sputnikovaya Sistema or Global Navigation Satellite System (GLONASS) both qualify as GNSS. Two other satellite localization systems, Galileo (European Union) and Compass (Chinese), are expected to achieve full global coverage capability by 2020. Detailed information on GNSS technology is plentiful, and there are many books that provide a complete description of these navigation systems [9- 11]. But the focus of this chapter is on the applications of GPS in agricultural operations. These applications include positioning of operating machines, soil sampling, variable rate application and vehicle guidance.Comisión Europea FP7/2007-201

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

Sensors and Technologies in Spain: State-of-the-Art

The aim of this special issue was to provide a comprehensive view on the state-of-the-art sensor technology in Spain. Different problems cause the appearance and development of new sensor technologies and vice versa, the emergence of new sensors facilitates the solution of existing real problems. [...

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time