Chapter Hybrid-Powered Autonomous Robots for Reducing Both Fuel Consumption and Pollution in Precision Agriculture Tasks

Environmental contamination and the resulting climate change are major concerns worldwide. Agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants. Thus, this study investigates techniques to reduce fuel consumption in robotic vehicles used for agricultural tasks and therefore reduce atmospheric emissions from these automated systems. A hybrid energy system for autonomous robots devoted to weed and pest control in agriculture is modeled and evaluated, and its exhaust emissions are compared with those of an internal combustion engine-powered system. Agricultural implements require power for hydraulic pumps and fans; this energy is conventionally provided by power take-off (PTO) systems, which waste substantial amounts of energy. In this work, we examine a solution by designing and assessing a hybrid energy system that omits the alternators from the original vehicle and modifies the agricultural implements to replace the PTO power with electrical power. The hybrid energy system uses the original combustion engine of the tractor in combination with a new electrical energy system based on a hydrogen fuel cell. We analyze and compare the exhaust gases resulting from the use of (1) an internal combustion engine as the single power source and (2) the hybrid energy system. The results demonstrate that the hybrid energy system reduced emissions by up to approximately 50%

Hybrid-Powered Autonomous Robots for Reducing Both Fuel Consumption and Pollution in Precision Agriculture Tasks

Environmental contamination and the resulting climate change are major concerns worldwide. Agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants. Thus, this study investigates techniques to reduce fuel consumption in robotic vehicles used for agricultural tasks and therefore reduce atmospheric emissions from these automated systems. A hybrid energy system for autonomous robots devoted to weed and pest control in agriculture is modeled and evaluated, and its exhaust emissions are compared with those of an internal combustion engine-powered system. Agricultural implements require power for hydraulic pumps and fans; this energy is conventionally provided by power take-off (PTO) systems, which waste substantial amounts of energy. In this work, we examine a solution by designing and assessing a hybrid energy system that omits the alternators from the original vehicle and modifies the agricultural implements to replace the PTO power with electrical power. The hybrid energy system uses the original combustion engine of the tractor in combination with a new electrical energy system based on a hydrogen fuel cell. We analyze and compare the exhaust gases resulting from the use of (1) an internal combustion engine as the single power source and (2) the hybrid energy system. The results demonstrate that the hybrid energy system reduced emissions by up to approximately 50%

Detailed Study of Amplitude Nonlinearity in Piezoresistive Force Sensors

This article upgrades the RC linear model presented for piezoresistive force sensors. Amplitude nonlinearity is found in sensor conductance, and a characteristic equation is formulated for modeling its response under DC-driving voltages below 1 V. The feasibility of such equation is tested on four FlexiForce model A201-100 piezoresistive sensors by varying the sourcing voltage and the applied forces. Since the characteristic equation proves to be valid, a method is presented for obtaining a specific sensitivity in sensor response by calculating the appropriate sourcing voltage and feedback resistor in the driving circuit; this provides plug-and-play capabilities to the device and reduces the start-up time of new applications where piezoresistive devices are to be used. Finally, a method for bypassing the amplitude nonlinearity is presented with the aim of reading sensor capacitance

Camera sensor arrangement for crop/weed detection accuracy in agronomic images

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects. © 2013 by the authors; licensee MDPI, Basel, Switzerland.The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986. This paper has been extended from a previous paper published in [20]. The authors wish also to acknowledge to the project AGL2011-30442-C02-02, supported by the Ministerio de Economía y Competitividad of Spain within the Plan Nacional de I+D+i.Peer Reviewe

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient

Pulmonary administration of a dry powder formulation of the antifibrotic drug tilorone reduces silica-induced lung fibrosis in mice

The aim of this work was to study the antifibrotic effect of pulmonary administration of tilorone to lung fibrosis. L-leucine coated tilorone particles were prepared and their aerosolization properties were analyzed using two dry powder inhalers (Easyhaler and Twister). In addition, the biological activity and cell monolayer permeation was tested. The antifibrotic effect of tilorone delivered by oropharyngeal aspiration was studied in vivo using a silica-induced model of pulmonary fibrosis in mice in a preventive setting. When delivered from the Easyhaler in an inhalation simulator, the emitted dose and fine particle fraction were independent from the pressure applied and showed dose repeatability. However, with Twister the aerosolization was pressure-dependent indicating poor compatibility between the device and the formulation. The formulation showed more consistent permeation through a differentiated Calu-3 cell monolayer compared to pristine tilorone. Tilorone decreased the histological fibrosis score in vivo in systemic and local administration, but only systemic administration decreased the mRNA expression of type I collagen. The difference was hypothesized to result from 40-fold higher drug concentration in tissue samples in the systemic administration group. These results show that tilorone can be formulated as inhalable dry powder and has potential as an oral and inhalable antifibrotic drug.Peer reviewe

Mejora de la calidad docente por la investigación en el sector industrial

Motivación del alumno mediante nuevas metodologías presenciales derivadas de la investigación aplicada y orientadas hacia el emprendimiento en el sector industrial. Las metodologías encaminadas a dar solución en los desarrollos de investigación se utilizan como ejemplos significativos en las materias que forman parte del proyecto

Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor’s positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects

Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy

Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus

A genome-wide association study identifies risk alleles in plasminogen and P4HA2 associated with giant cell arteritis

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analysed in 2,134 cases and 9,125 unaffected controls from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, P = 1.94E-54, per-allele OR = 1.79; and rs9275592, P = 1.14E-40, OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, P = 1.23E-10, OR = 1.28; and rs128738, P = 4.60E-09, OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis