Wireless ICT monitoring for hydroponic agriculture

It is becoming increasingly evident that agriculture is playing a pivotal role in the socio-economic development of South Africa. The agricultural sector is important because it contributes approximately 2% to the gross domestic product of the country. However, many factors impact on the sustainability of traditional agriculture in South Africa. Unpredictable climatic conditions, land degradation and a lack of information and awareness of innovative farming solutions are among the factors plaguing the South African agricultural landscape. Various farming techniques have been looked at in order to mitigate these challenges. Among these interventions are the introduction of organic agriculture, greenhouse agriculture and hydroponic agriculture, which is the focus area of this study. Hydroponic agriculture is a method of precision agriculture where plants are grown in a mineral nutrient solution instead labour- intensive activity that requires an incessant monitoring of the farm environment in order to ensure a successful harvest. Hydroponic agriculture, however, presents a number of challenges that can be mitigated by leveraging the recent mobile Information and Communication Technologies (ICTs) breakthroughs. This dissertation reports on the development of a wireless ICT monitoring application for hydroponic agriculture: HydroWatcher mobile app. HydroWatcher is a complex system that is composed of several interlacing parts and this study will be focusing on the development of the mobile app, the front-end of the system. This focus is motivated by the fact that in such systems the front-end, being the part that the users interact with, is critical for the acceptance of the system. However, in order to design and develop any part of HydroWatcher, it is crucial to understand the context of hydroponic agriculture in South Africa. Therefore, complementary objectives of this study are to identify the critical factors that impact hydroponic agriculture as well as the challenges faced by hydroponic farmers in South Africa. Thus, it leads to the elicitation of the requirements for the design and development of HydroWatcher. This study followed a mixed methods approach, including interviews, observations, exploration of hydroponic farming, to collect the data, which will best enable the researcher to understand the activities relating to hydroponic agriculture. A qualitative content analysis was followed to analyse the data and to constitute the requirements for the system and later to assert their applicability to the mobile app. HydroWatcher proposes to couple recent advances in mobile technology development, like the Android platform, with the contemporary advances in electronics necessary for the creation of wireless sensor nodes, as well as Human Computer interaction guidelines tailored for developing countries, in order to boost the user experience

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Numerical simulation of combustion instability: flame thickening and boundary conditions

Combustion-driven instabilities are a significant barrier for progress for many avenues of immense practical relevance in engineering devices, such as next generation gas turbines geared towards minimising pollutant emissions being susceptible to thermoacoustic instabilities. Numerical simulations of such reactive systems must try to balance a dynamic interplay between cost, complexity, and retention of system physics. As such, new computational tools of relevance to Large Eddy Simulation (LES) of compressible, reactive flows are proposed and evaluated. High order flow solvers are susceptible to spurious noise generation at boundaries which can be very detrimental for combustion simulations. Therefore Navier-Stokes Characteristic Boundary conditions are also reviewed and an extension to axisymmetric configurations proposed. Limitations and lingering open questions in the field are highlighted. A modified Artificially Thickened Flame (ATF) model coupled with a novel dynamic formulation is shown to preserve flame-turbulence interaction across a wide range of canonical configurations. The approach does not require efficiency functions which can be difficult to determine, impact accuracy and have limited regimes of validity. The method is supplemented with novel reverse transforms and scaling laws for relevant post-processing from the thickened to unthickened state. This is implemented into a wider Adaptive Mesh Refinement (AMR) context to deliver a unified LES-AMR-ATF framework. The model is validated in a range of test case showing noticeable improvements over conventional LES alternatives. The proposed modifications allow meaningful inferences about flame structure that conventionally may have been restricted to the domain of Direct Numerical Simulation. This allows studying the changes in small-scale flow and scalar topologies during flame-flame interaction. The approach is applied to a dual flame burner setup, where simulations show inclusion of a neighbouring burner increases compressive flow topologies as compared to a lone flame. This may lead to favouring convex scalar structures that are potentially responsible for the increase in counter-normal flame-flame interactions observed in experiments.Open Acces

Row-sensing Templates: A Generic 3D Sensor-based Approach to Robot Localization with Respect to Orchard Row Centerlines

Accurate robot localization relative to orchard row centerlines is essential for autonomous guidance where satellite signals are often obstructed by foliage. Existing sensor-based approaches rely on various features extracted from images and point clouds. However, any selected features are not available consistently, because the visual and geometrical characteristics of orchard rows change drastically when tree types, growth stages, canopy management practices, seasons, and weather conditions change. In this work, we introduce a novel localization method that doesn't rely on features; instead, it relies on the concept of a row-sensing template, which is the expected observation of a 3D sensor traveling in an orchard row, when the sensor is anywhere on the centerline and perfectly aligned with it. First, the template is built using a few measurements, provided that the sensor's true pose with respect to the centerline is available. Then, during navigation, the best pose estimate (and its confidence) is estimated by maximizing the match between the template and the sensed point cloud using particle-filtering. The method can adapt to various orchards and conditions by re-building the template. Experiments were performed in a vineyard, and in an orchard in different seasons. Results showed that the lateral mean absolute error (MAE) was less than 3.6% of the row width, and the heading MAE was less than 1.72 degrees. Localization was robust, as errors didn't increase when less than 75% of measurement points were missing. The results indicate that template-based localization can provide a generic approach for accurate and robust localization in real-world orchards

Fuel-efficient driving strategies

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches

Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations

open18siThe eddy covariance is a powerful technique to estimate the surface-Atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-The-Art processing methodsopenSabbatini, Simone; Mammarella, Ivan; Arriga, Nicola; Fratini, Gerardo; Graf, Alexander; Hörtnagl, Lukas; Ibrom, Andreas; Longdoz, Bernard; Mauder, Matthias; Merbold, Lutz; Metzger, Stefan; Montagnani, Leonardo; Pitacco, Andrea; Rebmann, Corinna; Sedlák, Pavel; Šigut, Ladislav; Vitale, Domenico; Papale, DarioSabbatini, Simone; Mammarella, Ivan; Arriga, Nicola; Fratini, Gerardo; Graf, Alexander; Hörtnagl, Lukas; Ibrom, Andreas; Longdoz, Bernard; Mauder, Matthias; Merbold, Lutz; Metzger, Stefan; Montagnani, Leonardo; Pitacco, Andrea; Rebmann, Corinna; Sedlák, Pavel; Šigut, Ladislav; Vitale, Domenico; Papale, Dari

Autonomous 3D Urban and Complex Terrain Geometry Generation and Micro-Climate Modelling Using CFD and Deep Learning

Sustainable building design requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction between complex urban geometries and the microclimate is characterized by complex transport mechanisms. The challenge to generate geometric and physics boundary conditions in an automated manner is hindering the progress of computational methods in urban design. Thus, the challenge of modelling realistic and pragmatic numerical urban micro-climate for wind engineering, environmental, and building energy simulation applications should address the complexity of the geometry and the variability of surface types involved in urban exposures. The original contribution to knowledge in this research is the proposed an end-to-end workflow that employs a cutting-edge deep learning model for image segmentation to generate building footprint polygons autonomously and combining those polygons with LiDAR data to generate level of detail three (LOD3) 3D building models to tackle the geometry modelling issue in climate modelling and solar power potential assessment. Urban and topography geometric modelling is a challenging task when undertaking climate model assessment. This paper describes a deep learning technique that is based on U-Net architecture to automate 3D building model generation by combining satellite imagery with LiDAR data. The deep learning model used registered a mean squared error of 0.02. The extracted building polygons were extruded using height information from corresponding LiDAR data. The building roof structures were also modelled from the same point cloud data. The method used has the potential to automate the task of generating urban scale 3D building models and can be used for city-wide applications. The advantage of applying a deep learning model in an image processing task is that it can be applied to a new set of input image data to extract building footprint polygons for autonomous application once it has been trained. In addition, the model can be improved over time with minimum adjustments when an improved quality dataset is available, and the trained parameters can be improved further building on previously learned features. Application examples for pedestrian level wind and solar energy availability assessment as well as modeling wind flow over complex terrain are presented