Sensors, Volume 15, Issue 2 (February 2015) – 125 articles , Pages 2232-4604

The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour. Full article

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. Full article

We present a small, compact and portable device for point-of-care instantaneous early detection of anemia. The method used is based on direct hematocrit measurement from whole blood samples by means of impedance analysis. This device consists of a custom electronic instrumentation and a plug-and-play disposable sensor. The designed electronics rely on straightforward standards for low power consumption, resulting in a robust and low consumption device making it completely mobile with a long battery life. Another approach could be powering the system based on other solutions like indoor solar cells, or applying energy-harvesting solutions in order to remove the batteries. The sensing system is based on a disposable low-cost label-free three gold electrode commercial sensor for 50 µL blood samples. The device capability for anemia detection has been validated through 24 blood samples, obtained from four hospitalized patients at Hospital Clínic. As a result, the response, effectiveness and robustness of the portable point-of-care device to detect anemia has been proved with an accuracy error of 2.83% and a mean coefficient of variation of 2.57% without any particular case above 5%. Full article

Assistive and rehabilitative powered exoskeletons for spinal cord injury (SCI) and stroke subjects have recently reached the clinic. Proper tension and joint alignment are critical to ensuring safety. Challenges still exist in adjustment and fitting, with most current systems depending on personnel experience for appropriate individual fastening. Paraplegia and tetraplegia patients using these devices have impaired sensation and cannot signal if straps are uncomfortable or painful. Excessive pressure and blood-flow restriction can lead to skin ulcers, necrotic tissue and infections. Tension must be just enough to prevent slipping and maintain posture. Research in pressure dynamics is extensive for wheelchairs and mattresses, but little research has been done on exoskeleton straps. We present a system to monitor pressure exerted by physical human-machine interfaces and provide data about levels of skin/body pressure in fastening straps. The system consists of sensing arrays, signal processing hardware with wireless transmission, and an interactive GUI. For validation, a lower-body powered exoskeleton carrying the full weight of users was used. Experimental trials were conducted with one SCI and one able-bodied subject. The system can help prevent skin injuries related to excessive pressure in mobility-impaired patients using powered exoskeletons, supporting functionality, independence and better overall quality of life. Full article

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques. Full article

Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration. Full article

Recent technological advancements have led to the production of arrays of miniaturized sensors, often embedded in existing multitasking devices (e.g., smartphones, tablets) and using a wide range of radio standards (e.g., Bluetooth, Wi-Fi, 4G cellular networks). Altogether, these technological evolutions coupled with the diffusion of ubiquitous Internet connectivity provide the base-line technology for the Internet of Things (IoT). The rapid increase of IoT devices is enabling the definition of new paradigms of data collection and introduces the concept of mobile crowd-sensing. In this respect, new sensing methodologies promise to extend the current understanding of the environment and social behaviors by leveraging citizen-contributed data for a wide range of applications. Environmental sensing can however only be successful if all the heterogeneous technologies and infrastructures work smoothly together. As a result, the interconnection and orchestration of devices is one of the central issues of the IoT paradigm. With this in mind, we propose an approach for improving the accessibility of observation data, based on interoperable standards and on-device web services. Full article

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices. Full article

An all-digital on-chip delay sensor (OCDS) circuit with high delay-measurement resolution and low supply-voltage sensitivity for efficient detection and diagnosis in high-performance electronic system applications is presented. Based on the proposed delay measurement scheme, the quantization resolution of the proposed OCDS can be reduced to several picoseconds. Additionally, the proposed cascade-stage delay measurement circuit can enhance immunity to supply-voltage variations of the delay measurement resolution without extra self-biasing or calibration circuits. Simulation results show that the delay measurement resolution can be improved to 1.2 ps; the average delay resolution variation is 0.55% with supply-voltage variations of ±10%. Moreover, the proposed delay sensor can be implemented in an all-digital manner, making it very suitable for high-performance electronic system applications as well as system-level integration. Full article

In this paper we propose a soft-hard combination scheme, called SHC scheme, for cooperative spectrum sensing in cognitive radio networks. The SHC scheme deploys a cluster based network in which Likelihood Ratio Test (LRT)-based soft combination is applied at each cluster, and weighted decision fusion rule-based hard combination is utilized at the fusion center. The novelties of the SHC scheme are as follows: the structure of the SHC scheme reduces the complexity of cooperative detection which is an inherent limitation of soft combination schemes. By using the LRT, we can detect primary signals in a low signal-to-noise ratio regime (around an average of −15 dB). In addition, the computational complexity of the LRT is reduced since we derive the closed-form expression of the probability density function of LRT value. The SHC scheme also takes into account the different effects of large scale fading on different users in the wide area network. The simulation results show that the SHC scheme not only provides the better sensing performance compared to the conventional hard combination schemes, but also reduces sensing overhead in terms of reporting time compared to the conventional soft combination scheme using the LRT. Full article

The Kalman filter (KF) has always been used to improve north-finding performance under practical conditions. By analyzing the characteristics of the azimuth rotational inertial measurement unit (ARIMU) on a stationary base, a linear state equality constraint for the conventional KF used in the fine north-finding filtering phase is derived. Then, a constrained KF using the state equality constraint is proposed and studied in depth. Estimation behaviors of the concerned navigation errors when implementing the conventional KF scheme and the constrained KF scheme during stationary north-finding are investigated analytically by the stochastic observability approach, which can provide explicit formulations of the navigation errors with influencing variables. Finally, multiple practical experimental tests at a fixed position are done on a postulate system to compare the stationary north-finding performance of the two filtering schemes. In conclusion, this study has successfully extended the utilization of the stochastic observability approach for analytic descriptions of estimation behaviors of the concerned navigation errors, and the constrained KF scheme has demonstrated its superiority over the conventional KF scheme for ARIMU stationary north-finding both theoretically and practically. Full article

In this work a novel bioassay for mustard agent detection was proposed. The bioassay is based on the capability of these compounds to inhibit the enzyme choline oxidase. The enzymatic activity, which is correlated to the mustard agents, was electrochemically monitored measuring the enzymatic product, hydrogen peroxide, by means of a screen-printed electrode modified with Prussian Blue nanoparticles. Prussian Blue nanoparticles are able to electrocatalyse the hydrogen peroxide concentration reduction at low applied potential (−50 mV vs. Ag/AgCl), thus allowing the detection of the mustard agents with no electrochemical interferences. The suitability of this novel bioassay was tested with the nitrogen mustard simulant bis(2-chloroethyl)amine and the sulfur mustard simulants 2-chloroethyl ethyl sulfide and 2-chloroethyl phenyl sulfide. The bioassay proposed in this work allowed the detection of mustard agent simulants with good sensitivity and fast response, which are excellent premises for the development of a miniaturised sensor well suited for an alarm system in case of terrorist attacks. Full article

We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement. Full article

Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method. Full article

A rapid, simple, sensitive, and specific immunochromatographic test strip was developed for the detection of Pantoea stewartii subsp. stewartii (Pss) in corn seed which was soaked overnight and then centrifuged for precipitate re-dissolved as samples. A pair of sensitive monoclonal antibodies for the immunochromatographic test strip was generated by mice immunization and cell fusion. Under optimized conditions, the lower detection limit of the strips for Pss was 1 × 10⁵ cfu/mL both in 0.01 M phosphate buffer solution and corn seed samples, with no cross-reactivity with other common plant pathogens. The developed strip is useful and rapid for the detection of Pss in corn seed samples. Full article

Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. Full article

We demonstrate the application of polymer planar Bragg gratings for multi-axial strain sensing and particularly highlight simultaneous 2D strain measurement. A polymer planar Bragg grating (PPBG) fabricated with a single writing step in bulk polymethylmethacrylate is used for measuring both tensile and compressive strain at various angles. It is shown that the sensitivity of the PPBG strongly depends on the angle between the optical waveguide into which the grating is inscribed and the direction along which the mechanical load is applied. Additionally, a 2D PPBG fabricated by writing two Bragg gratings angularly displaced from each other into a single polymer platelet is bonded to a stainless steel plate. The two reflected wavelengths exhibit different sensitivities while tested toward tensile and compressive strain. These characteristics make 2D PPBG suitable for measuring multi-axial tensile and compressive strain. Full article

We describe a Complementary Metal-Oxide Semiconductor (CMOS) differential interface circuit for capacitive Micro-Electro-Mechanical Systems (MEMS) pressure sensors that is functional over a wide temperature range between −55 °C and 225 °C. The circuit is implemented using IBM 0.13 μm CMOS technology with 2.5 V power supply. A constant-g_m biasing technique is used to mitigate performance degradation at high temperatures. The circuit offers the flexibility to interface with MEMS sensors with a wide range of the steady-state capacitance values from 0.5 pF to 10 pF. Simulation results show that the circuitry has excellent linearity and stability over the wide temperature range. Experimental results confirm that the temperature effects on the circuitry are small, with an overall linearity error around 2%. Full article

The measurement of ionizing radiation (IR) is a crucial issue in different areas of interest, from environmental safety and industrial monitoring to aerospace and medicine. Optical fiber sensors have recently proven good candidates as radiation dosimeters. Here we investigate the effect of IR on germanosilicate optical fibers. A piece of Ge-doped fiber enclosed between two fiber Bragg gratings (FBGs) is irradiated with gamma radiation generated by a 6 MV medical linear accelerator. With respect to other FBG-based IR dosimeters, here the sensor is only the bare fiber without any special internal structure. A near infrared laser is frequency locked to the cavity modes for high resolution measurement of radiation induced effects on the fiber optical parameters. In particular, we observe a variation of the fiber thermo-optic response with the radiation dose delivered, as expected from the interaction with Ge defect centers, and demonstrate a detection limit of 360 mGy. This method can have an impact in those contexts where low radiation doses have to be measured both in small volumes or over large areas, such as radiation therapy and radiation protection, while bare optical fibers are cheap and disposable. Full article

Microbial biofilm colonies will in many cases form a smart material capable of responding to external threats dependent on their size and internal state. The microbial community accordingly switches between passive, protective, or attack modes of action. In order to decide which strategy to employ, it is essential for the biofilm community to be able to sense its own size. The sensor designed to perform this task is termed a quorum sensor, since it only permits collective behaviour once a sufficiently large assembly of microbes have been established. The generic quorum sensor construct involves two genes, one coding for the production of a diffusible signal molecule and one coding for a regulator protein dedicated to sensing the signal molecules. A positive feedback in the signal molecule production sets a well-defined condition for switching into the collective mode. The activation of the regulator involves a slow dimerization, which allows low-pass filtering of the activation of the collective mode. Here, we review and combine the model components that form the basic quorum sensor in a number of Gram-negative bacteria, e.g., Pseudomonas aeruginosa. Full article

Flexible iridium oxide (IrO_x)-based micro-electrodes were fabricated on flexible polyimide substrates using a sol-gel deposition process for utilization as integrated pseudo-reference electrodes for bio-electrochemical sensing applications. The fabrication method yields reliable miniature on-probe IrO_x electrodes with long lifetime, high stability and repeatability. Such sensors can be used for long-term measurements. Various dimensions of sol-gel iridium oxide electrodes including 1 mm × 1 mm, 500 µm × 500 µm, and 100 µm × 100 µm were fabricated. Sensor longevity and pH dependence were investigated by immersing the electrodes in hydrochloric acid, fetal bovine serum (FBS), and sodium hydroxide solutions for 30 days. Less pH dependent responses, compared to IrO_x electrodes fabricated by electrochemical deposition processes, were measured at 58.8 ± 0.4 mV/pH, 53.8 ± 1.3 mV/pH and 48 ± 0.6 mV/pH, respectively. The on-probe IrO_x pseudo-reference electrodes were utilized for dopamine sensing. The baseline responses of the sensors were higher than the one using an external Ag/AgCl reference electrode. Using IrO_x reference electrodes integrated on the same probe with working electrodes eliminated the use of cytotoxic Ag/AgCl reference electrode without loss in sensitivity. This enables employing such sensors in long-term recording of concentrations of neurotransmitters in central nervous systems of animals and humans. Full article

Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment. Full article

Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach. Full article

Field spectroradiometers integrated in automated systems at Eddy Covariance (EC) sites are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. However, exposure to varying environmental conditions can affect the functioning of these sensors, especially if these cannot be completely insulated and stabilized. This can cause inaccuracy in the spectral measurements and hinder the comparison between data acquired at different sites. This paper describes the characterization of key sensor models in a double beam spectroradiometer necessary to calculate the Hemispherical-Conical Reflectance Factor (HCRF). Dark current, temperature dependence, non-linearity, spectral calibration and cosine receptor directional responses are modeled in the laboratory as a function of temperature, instrument settings, radiation measured or illumination angle. These models are used to correct the spectral measurements acquired continuously by the same instrument integrated outdoors in an automated system (AMSPEC-MED). Results suggest that part of the instrumental issues cancel out mutually or can be controlled by the instrument configuration, so that changes induced in HCFR reached about 0.05 at maximum. However, these corrections are necessary to ensure the inter-comparison of data with other ground or remote sensors and to discriminate instrumentally induced changes in HCRF from those related with vegetation physiology and directional effects. Full article

In this paper, we present a system simulation framework for the design and performance evaluation of complex wireless cyber-physical systems. We describe the simulator architecture and the specific developments that are required to simulate cyber-physical systems relying on multi-channel, multihop mesh networks. We introduce realistic and efficient physical layer models and a system simulation methodology, which provides statistically significant performance evaluation results with low computational complexity. The capabilities of the proposed framework are illustrated in the example of WirelessHART, a centralized, real-time, multi-hop mesh network designed for industrial control and monitor applications. Full article

In recent years sensor components have been extending classical computer-based support systems in a variety of applications domains (sports, health, etc.). In this article we review the use of sensors for the application domain of learning. For that we analyzed 82 sensor-based prototypes exploring their learning support. To study this learning support we classified the prototypes according to the Bloom’s taxonomy of learning domains and explored how they can be used to assist on the implementation of formative assessment, paying special attention to their use as feedback tools. The analysis leads to current research foci and gaps in the development of sensor-based learning support systems and concludes with a research agenda based on the findings. Full article

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH₄ and CO₂. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO₂ concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO₂ sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology. Full article

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense. Full article

This paper is concerned with the digitization and visualization of potted greenhouse tomato plants in indoor environments. For the digitization, an inexpensive and efficient commercial stereo sensor—a Microsoft Kinect—is used to separate visual information about tomato plants from background. Based on the Kinect, a 4-step approach that can automatically detect and segment stems of tomato plants is proposed, including acquisition and preprocessing of image data, detection of stem segments, removing false detections and automatic segmentation of stem segments. Correctly segmented texture samples including stems and leaves are then stored in a texture database for further usage. Two types of tomato plants—the cherry tomato variety and the ordinary variety are studied in this paper. The stem detection accuracy (under a simulated greenhouse environment) for the cherry tomato variety is 98.4% at a true positive rate of 78.0%, whereas the detection accuracy for the ordinary variety is 94.5% at a true positive of 72.5%. In visualization, we combine L-system theory and digitized tomato organ texture data to build realistic 3D virtual tomato plant models that are capable of exhibiting various structures and poses in real time. In particular, we also simulate the growth process on virtual tomato plants by exerting controls on two L-systems via parameters concerning the age and the form of lateral branches. This research may provide useful visual cues for improving intelligent greenhouse control systems and meanwhile may facilitate research on artificial organisms. Full article