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BACKGROUND: Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor‐intensive and time‐consuming, the objective of this study was to investigate the potential of near‐infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products. RESULTS: NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000–4000 cm−1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes. CONCLUSION: These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Mechanical damage of fresh fruit occurs throughout the postharvest supply chain leading to poor consumer acceptance and marketability. In this review, the mechanisms of damage development are discussed first. Mathematical modeling provides advanced ways to describe and predict the deformation of fruit with arbitrary geometry, which is important to understand their mechanical responses to external forces. Also, the effects of damage at the cellular and molecular levels are discussed as this provides insight into fruit physiological responses to damage. Next, direct measurement methods for damage including manual evaluation, optical detection, magnetic resonance imaging, and X-ray computed tomography are examined, as well as indirect methods based on physiochemical indexes. Also, methods to measure fruit susceptibility to mechanical damage based on the bruise threshold and the amount of damage per unit of impact energy are reviewed. Further, commonly used external and interior packaging and their applications in reducing damage are summarized, and a recent biomimetic approach for designing novel lightweight packaging inspired by the fruit pericarp. Finally, future research directions are provided. Mathematical modeling has been increasingly used to calculate damage to fruit. Cell and molecular mechanisms response to fruit damage is an under-explored area. Susceptibility measurement of different mechanical forces has received attention. Customized design of reusable and biodegradable packaging is a hot topic of research. [ABSTRACT FROM AUTHOR]

Chloroplasts movement within mesophyll cells in C4 plants is hypothesized to enhance the CO2 concentrating mechanism, but this is difficult to verify experimentally. A three-dimensional (3D) leaf model can help analyse how chloroplast movement influences the operation of the CO2 concentrating mechanism. The first volumetric reaction–diffusion model of C4 photosynthesis that incorporates detailed 3D leaf anatomy, light propagation, ATP and NADPH production, and CO2, O2 and bicarbonate concentration driven by diffusional and assimilation/emission processes was developed. It was implemented for maize leaves to simulate various chloroplast movement scenarios within mesophyll cells: the movement of all mesophyll chloroplasts towards bundle sheath cells (aggregative movement) and movement of only those of interveinal mesophyll cells towards bundle sheath cells (avoidance movement). Light absorbed by bundle sheath chloroplasts relative to mesophyll chloroplasts increased in both cases. Avoidance movement decreased light absorption by mesophyll chloroplasts considerably. Consequently, total ATP and NADPH production and net photosynthetic rate increased for aggregative movement and decreased for avoidance movement compared with the default case of no chloroplast movement at high light intensities. Leakiness increased in both chloroplast movement scenarios due to the imbalance in energy production and demand in mesophyll and bundle sheath cells. These results suggest the need to design strategies for coordinated increases in electron transport and Rubisco activities for an efficient CO2 concentrating mechanism at very high light intensities. [ABSTRACT FROM AUTHOR]

This paper presents porous polydimethylsiloxane (PDMS) optical phantoms with tunable microstructural and optical properties to mimic porous biological tissues (e.g., fruit) during the design and optimization of novel optical setups. A well connected salt network formed using salt particles of various size distributions was used to obtain porous PDMS phantoms of different porous features including porosity, pore size distribution, pore number density and pore connectivity. These microstructural features are strongly related to the light scattering from the phantom where a higher reduced scattering coefficient (μs′) was observed from the porous PDMS phantom with a higher number of small pores compared to the optical phantom with a lower number of larger pores. The prepared phantoms were used to validate GASMAS (gas in scattering media absorption spectroscopy) H2O and O2 sensors by quantifying the optical path length through the pores and the O2 concentration inside the pores. [ABSTRACT FROM AUTHOR]

Photosynthetic active radiation (PAR) refers to photons between 400 and 700 nm. These photons drive photosynthesis, providing carbohydrates for plant metabolism and development. Far-red radiation (FR, 701–750 nm) is excluded in this definition because no FR is absorbed by the plant photosynthetic pigments. However, including FR in the light spectrum provides substantial benefits for biomass production and resource-use efficiency. We investigated the effects of continuous FR addition and end-of-day additional FR to a broad white light spectrum (BW) on carbohydrate concentrations in the top and bottom leaves of sweet basil (Ocimum basilicum L.), a species that produces the raffinose family oligosaccharides raffinose and stachyose and preferentially uses the latter as transport sugar. Glucose, fructose, sucrose, raffinose, and starch concentrations increased significantly in top and bottom leaves with the addition of FR light. The increased carbohydrate pools under FR light treatments are associated with more efficient stachyose production and potentially improved phloem loading through increased sucrose homeostasis in intermediary cells. The combination of a high biomass yield, increased resource-use efficiency, and increased carbohydrate concentration in leaves in response to the addition of FR light offers opportunities for commercial plant production in controlled growth environments. [ABSTRACT FROM AUTHOR]

Eggshell strength is a critical quality factor for consumption eggs as it affects the probability of breakage in practice. In this study, a fast and low-cost methodology for the non-destructive determination of eggshell strength is presented. The method utilized a small steel ball to impact the egg and a microphone to analyse the impact characteristics. Hertz contact theory was applied to relate the measured impact characteristics to the local stiffness of the eggshell. Therefore, a total of 150 eggs were studied on which eight consecutive measurements per egg were taken around the equator at equidistant places. The results showed a strong correlation of 0.93 between the traditional static stiffness measured during quasi-static compression tests and the average stiffness obtained from the new methodology. This paves the way towards fast, low-cost and non-destructive in-line shell strength measurements to reduce the number of cracked eggs reaching the consumer. [ABSTRACT FROM AUTHOR]

In the egg industry, fast and highly reliable quality measurements are crucial. This study presents a novel method based on Hertz contact theory that allows for non-destructive determination of eggshell strength. The goal of the study was to evaluate the material strength (Young's Modulus) and structural strength (stiffness) of eggshells. To this end, an experimental setup was constructed to measure the collision of an eggshell with a small steel ball, which was recorded using a laser vibrometer. The study analyzed a sample of 120 eggs and found a correlation of 0.85 between the traditional static stiffness measured during quasi-static compression tests and the stiffness obtained from the Hertz contact theory. The results show that Hertz contact theory is valid for small steel spheres impacting eggshells, while a sensitivity analysis indicated that the most important factor in determining the strength of the eggshell is the contact duration between the egg and the impactor. These results open up the possibility of grading eggs based on their shell strength in a non-destructive manner. [ABSTRACT FROM AUTHOR]

Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near‐infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS‐DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four‐class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two‐class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein‐ and water‐related changes were found important to distinguish the fillets. Based on these findings, the four‐class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two‐class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. Practical Application: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output. [ABSTRACT FROM AUTHOR]

In this Quality Quandaries, we briefly introduce the concept of Digital Twins and provide the reader two real-life examples to get an idea about the versatility of it. We also highlight the role of statistics in this rapidly emerging field. [ABSTRACT FROM AUTHOR]

The spotted wing Drosophila (SWD), Drosophila suzukii , is a significant invasive pest of berries and soft-skinned fruits that causes major economic losses in fruit production worldwide. Automatic identification and monitoring strategies would allow to detect the emergence of this pest in an early stage and minimize its impact. The small size of Drosophila suzukii and similar flying insects makes it difficult to identify them using camera systems. Therefore, an optical sensor recording wingbeats was investigated in this study. We trained convolutional neural network (CNN) classifiers to distinguish D. suzukii insects from one of their closest relatives, Drosophila Melanogaster , based on their wingbeat patterns recorded by the optical sensor. Apart from the original wingbeat time signals, we modeled their frequency (power spectral density) and time-frequency (spectrogram) representations. A strict validation procedure was followed to estimate the models' performance in field-conditions. First, we validated each model on wingbeat data that was collected under the same conditions using different insect populations to train and test them. Next, we evaluated their robustness on a second independent dataset which was acquired under more variable environmental conditions. The best performing model, named "InceptionFly," was trained on wingbeat time signals. It was able to discriminate between our two target insects with a balanced accuracy of 92.1% on the test set and 91.7% on the second independent dataset. This paves the way towards early, automated detection of D. suzukii infestation in fruit orchards. [ABSTRACT FROM AUTHOR]

This article draws attention to key developments in the field of computer vision for industrial quality control and provides some illustrative examples. [ABSTRACT FROM AUTHOR]

We present the development of a bilinear regression model for multivariate calibration on the basis of maximum correntropy criteria (MCC) whose robustness can be easily controlled. MCC regression methods can be more effective when the assumption of normality does not hold or when data are contaminated with outliers. These methods are competitive when the degree of robustness against outliers should be controlled. By controlling the robustness, information from candidate outliers can be partially retained rather than completely included or discarded during calibration. Within the context of bilinear regression models, an MCC approach using statistically inspired modification of the partial least squares (SIMPLS) is proposed, which is named maximum correntropy‐weighted partial least squares (MCW‐PLS). Thanks to the controllable robustness of MCC models, observations are upweighted or downweighted during the calibration process, rendering robust models with soft discrimination of samples. Such a weighting represents an important advantage, especially for cases when samples are not drawn from a normal distribution. Applications to three real case studies are presented. These applications uncovered three main features of MCW‐PLS: robustness control between SIMPLS and robust SIMPLS (RSIMPLS), improvements in prediction performance of bilinear calibration models, and the possibility to detect the most informative samples in a calibration set. [ABSTRACT FROM AUTHOR]

In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process. [ABSTRACT FROM AUTHOR]

This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, implemented for the NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which is a traditional and cheap method. The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications. Moreover, the ISL-LMPC produces results of a quality comparable to the NMHE-NMPC framework at a significantly reduced computational cost. [ABSTRACT FROM AUTHOR]

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was €2.57 per % less missed lame cows, €1.65 per % less false alerts, and €12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system's potential adoption rate. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy offers a promising technological platform for continuous glucose monitoring in the human body. Moreover, these measurements could be performed in vivo with an implantable single-chip based optical sensor. However, a thin tissue layer may grow in the optical path of the sensor. As most biological tissues are highly scattering, they only allow a small fraction of the collimated light to pass, significantly reducing the light throughput. To quantify the effect of a thin tissue layer in the optical path, the bulk optical properties of serum and tissue samples grown on implanted dummy sensors were characterized using double integrating sphere and unscattered transmittance measurements. The estimated bulk optical properties were then used to calculate the light attenuation through a thin tissue layer. The combination band of glucose was found to be the better option, relative to the first overtone band, as the absorptivity of glucose molecules is higher, while the reduction in unscattered transmittance due to tissue growth is less. Additionally, as the wound tissue was found to be highly scattering, the unscattered transmittance of the tissue layer is expected to be very low. Therefore, a sensor configuration which measures the diffuse transmittance and/or reflectance instead was recommended. [ABSTRACT FROM AUTHOR]

Optical brain stimulation gained a lot of attention in neuroscience due to its superior cell-type specificity. In the design of illumination strategies, predicting the light propagation in a specific tissue is essential and requires knowledge of the optical properties of that tissue. We present the estimated absorption and reduced scattering in rodent brain tissue using non-destructive contact spatially resolved spectroscopy (cSRS). The obtained absorption and scattering in the cortex, hippocampus and striatum are similar, but lower than in the thalamus, leading to a less deep but broader light penetration profile in the thalamus. Next, the light distribution was investigated for different stimulation protocols relevant for fiber-optic based optogenetic experiments, using Monte Carlo simulation. A protocol specific analysis is proposed to evaluate the potential of thermally induced side effects. [ABSTRACT FROM AUTHOR]

In this study a hyperspectral imaging system (short wave infrared range from 1000 to 2500 nm) was used to model fish texture by experimental compression test. Partial least square–discriminate analysis modeling technique was used for classifying the samples by linking the hyperspectral information and their measured texture. TheR2of cross validation and prediction were 0.97 and 0.96, respectively. The root mean squared errors for cross validation and prediction were 0.07 and 0.09, respectively. Sensitivity and specificity for both class I and II were 1.00. Results indicated that hyperspectral imaging in short wave infrared range has ability to detect texture stiffness of rainbow trouts which is affected by freshness. [ABSTRACT FROM PUBLISHER]

This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor–trailer system track both linear and curvilinear target trajectories with low tracking error. [ABSTRACT FROM PUBLISHER]

In this study, the optical properties of potato flesh tissue were estimated using double-integrating sphere (DIS) measurements combined with an inverse adding-doubling (IAD) light propagation model. Total reflectance, total transmittance, and unscattered transmittance were measured for the wavelength range 500-1900 nm with 5-nm resolution. From these measurements, the bulk optical properties (absorption coefficient, scattering coefficient, and anisotropy factor) of 53 potato tubers of the Hermes cultivar were estimated. The estimated absorption coefficient spectra were dominated by water and starch absorption bands, the main chemical components of potato tissue. Comparison of these values to those reported in literature for similar products showed comparable absorption profiles. The obtained scattering coefficient spectra showed a smooth decrease from 166 to 160 cm in the near-infrared (NIR) spectral range with increasing wavelength, which is common for biological tissues. The anisotropy factor spectra obtained for the full wavelength range studied ranged between 0.949 and 0.959 with a maximum variability of 0.009 among the set of samples used. The information obtained in this study is essential to understand the effects of absorption and scattering on the propagation of light through the potato tubers in order to design more efficient sensors for non-destructive quality evaluation. [ABSTRACT FROM AUTHOR]

We present a combined three-dimensional (3-D) model of light propagation, CO2 diffusion and photosynthesis in tomato (Solanum lycopersicum L.) leaves. The model incorporates a geometrical representation of the actual leaf microstructure that we obtained with synchrotron radiation X-ray laminography, and was evaluated using measurements of gas exchange and leaf optical properties. The combination of the 3-D microstructure of leaf tissue and chloroplast movement induced by changes in light intensity affects the simulated CO2 transport within the leaf. The model predicts extensive reassimilation of CO2 produced by respiration and photorespiration. Simulations also suggest that carbonic anhydrase could enhance photosynthesis at low CO2 levels but had little impact on photosynthesis at high CO2 levels. The model confirms that scaling of photosynthetic capacity with absorbed light would improve efficiency of CO2 fixation in the leaf, especially at low light intensity. [ABSTRACT FROM AUTHOR]

The storage potential of apples highly depends on the maturity at harvest. Optical methods have been proposed to measure maturity in a fast, reliable and non-destructive way. However, the signal is often composed of photons with different penetration depths into the material. An attempt to separate these photons might result in more precise correlations with quality attributes, as these could relate to a specific layer/depth into the sample. Therefore, a Vis/NIR spatially resolved laser reflectance setup was used, combining a supercontinuum laser and a monochromator to illuminate samples with a monochrome focused beam in the 550-1000 nm wavelength range. A panchromatic camera was used to obtain diffuse reflectance profiles at each wavelength. In the period starting 50 days before until 11 days after commercial harvest, 320 Braeburn apples were measured. Partial least squares regression models were developed to relate apple maturity/quality to the diffuse reflectance spectra at different distances from the illumination point. The effect of detector size (spatial bandwidth) was also evaluated. A bandwidth of 0.82 mm in combination with a parameter specific illumination-detection distance, gave the best results. Using an internal test set, an R of prediction of 0.98 and 0.93, and a ratio of prediction to performance (RPD) of 5.84 and 3.42, predicting, respectively, the Streif index and starch conversion values was obtained. The predictions of soluble solids content (SSC) ( R of 0.81; RPD of 2.04) and firmness ( R of 0.65; RPD of 1.66) were less accurate. Also, worse predictions were obtained using an external test set. [ABSTRACT FROM AUTHOR]

Despite the research on opportunities toautomatically measure lameness in cattle, lameness detection systems are notwidely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat themproperly and in a timely fashion. Many papers have focused on the automatedmeasurement of gait or behavioral cow characteristics related to lameness. Inorder for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cowsneed to be developed and validated. Few studies have reached this latter stageof the development process. Also, comparison between the different approachesis impeded by the wide range of practical settings used to measure the gait or behavioralcharacteristic (e.g., measurements during normal farming routine or duringexperiments; cows guided or walking at their own speed) and by the differentdefinitions of lame cows. In the majority of the publications, mildly lame cowsare included in the non-lame cow group, which limits the possibility of alsodetecting early lameness cases. In this review, studies that used sensortechnology to measure changes in gait or behavior of cows related to lamenessare discussed together with practical considerations when conducting lamenessresearch. In addition, other prerequisites for any lameness detection system onfarms (e.g., need for early detection, real-time measurements) are discussed. [ABSTRACT FROM AUTHOR]

Due to its detrimental effect on cow welfare, health and production, lameness in dairy cows has received quite a lot of attention in the last few decades-not only in terms of prevention and treatment of lameness but also in terms of detection, as early treatment might decrease the number of severely lame cows in the herds as well as decrease the direct and indirect costs associated with lameness cases. Generally, lame cows are detected by the herdsman, hoof trimmer or veterinarian based on abnormal locomotion, abnormal behavior or the presence of hoof lesions during routine trimming. In the scientific literature, several guidelines are proposed to detect lame cows based on visual interpretation of the locomotion of individual cows (i.e., locomotion scoring systems). Researchers and the industry have focused on automating such observations to support the farmer in finding the lame cows in their herds, but until now, such automated systems have rarely been used in commercial herds. This review starts with the description of normal locomotion of cows in order to define 'abnormal' locomotion caused by lameness. Cow locomotion (gait and posture) and behavioral features that change when a cow becomes lame are described and linked to the existing visual scoring systems. In addition, the lack of information of normal cow gait and a clear description of 'abnormal' gait are discussed. Finally, the different set-ups used during locomotion scoring and their influence on the resulting locomotion scores are evaluated. [ABSTRACT FROM AUTHOR]

A rapid, robust, unbiased, and inexpensive discriminant method capable of classifying hazelnut ( Corylus avellana, L.) in compliance with the statements set out by the Commission Regulation (EC) No. 1284/2002 is economically important to the fresh and processed industries. Thus, in this study, the feasibility of high dynamic range (HDR) hyperspectral imaging for hazelnut kernel sorting (cv. Tonda Gentile Romana) of four quality classes (Class Extra, Class I, Class II, and Waste) has been investigated. Two different exposure times (5 and 8 ms) were selected for experiments, and the respective spectra were combined to obtain a HDR over the full spectral range. The illumination setup was optimized to improve the intensity and uniformity of the light along the field of view of the camera. PLS-DA was used to classify the kernels based on their spectra and the spectral pretreatment was optimized through an iterative routine. The performance of each PLS-DA model was defined based on its accuracy, sensitivity, and selectivity rates. All of the selected models provided a very good (>90 %) or good (>80 %) sensitivity and selectivity for the predefined classes. Misclassified kernels were primarily assigned to the low-quality classes (i.e., Class II and Waste). Moreover, the spatial domain was used to evaluate the feasibility of distinguishing hazelnut classes on the basis of their size and shape. It was found that hazelnut dimensions can be used to improve the accuracy of the classification of the kernels. Thanks to this combination of both spectral and spatial information spectral imaging could be used for quality sorting of hazelnuts. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the tractor–trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field. [ABSTRACT FROM PUBLISHER]

Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal yield. Autonomous navigation of an agricultural vehicle involves the control of different dynamic subsystems, such as the yaw angle dynamics and the longitudinal speed dynamics. In this study, a proportional-integral-derivative controller is used to control the longitudinal velocity of the tractor. For the control of the yaw angle dynamics, a proportional-derivative controller works in parallel with a type-2 fuzzy neural network. In such an arrangement, the former ensures the stability of the related subsystem, while the latter learns the system dynamics and becomes the leading controller. In this way, instead of modeling the interactions between the subsystems prior to the design of a model-based control, we develop a control algorithm which learns the interactions online from the measured feedback error. In addition to the control of the stated subsystems, a kinematic controller is needed to correct the errors in both the x- and the y- axis for the trajectory tracking problem of the tractor. To demonstrate the real-time abilities of the proposed control scheme, an autonomous tractor is equipped with the use of reasonably priced sensors and actuators. Experimental results show the efficacy and the efficiency of the proposed learning algorithm. [ABSTRACT FROM PUBLISHER]

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory. [ABSTRACT FROM AUTHOR]

Alternating least squares (ALS) is a blind source separation method commonly used in chemometrics to simultaneously estimate the absorption spectrum and concentration of different components in a chemical sample. In this study, the transferability of ALS from chemometrics to agricultural remote sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the subpixel cover distribution of different components, but fail to provide an estimate of pure spectral signature of the crop component. This info is, however, highly valuable, as this pure crop signature could be used to monitor the health status of trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of different components in a mixed image pixel. We tested the performance of ALS on binary synthetic mixtures of citrus canopy and soil spectra, as well as on a ray-tracing experiment of a virtual orchard. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE=0.05), as well as the pure spectral signatures of different endmembers (RRMSE<;0.12), and considerably improved the extraction of biophysical parameters (Δ R2 up to 0.43). Thus, ALS provides a promising new image analysis tool for agricultural remote sensing. [ABSTRACT FROM PUBLISHER]

Quantitative structure-activity relationship ( QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization ( PSO) and genetic algorithm ( GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression ( MLR)] and non-linear [locally weighted regression ( LWR) based on both Euclidean ( E) and Mahalanobis ( M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50. The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction ( RMSEP) for the test sets obtained by PSO/ MLR, GA/ MLR, PSO/ LWRE, PSO/ LWRM, GA/ LWRE, and GA/ LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear. [ABSTRACT FROM AUTHOR]

Thinning of fruit-tree blossoms is used to regulate the yearly tree bearing and to increase the fruit yield and quality. While this is still mostly done by hand, the increasing costs of manual labor have created a demand for mechanization. This has recently led to the development of several prototype thinning machines. The main disadvantage of these machines is that they are not selective, while the fruit bearing capacity of different floral buds is not equal. On-line information about the position and distribution of the floral buds on the tree can improve the efficiency of mechanized thinning. Therefore, the aim of this study was to identify the most informative wavebands to develop a multispectral vision sensor for detection of the floral buds of the pear cultivar Conference. Hyperspectral scans were taken from tree samples in five early phenological stages to create a database of reflectance spectra for the different tree features. A stepwise algorithm was then applied to this training set to select the best combination of wavebands having the highest discriminating power between the components of interest. Subsequently, canonical correlation analysis was used to create discriminant functions out of the selected wavebands. It was possible to correctly classify 95 % of the (pixel) observations with six selected wavebands. The discrimination performance was also tested as a function of the number of used wavebands. Analysis showed that when only the two most important wavebands were used, still over 90 % of the (pixel) observations could be correctly classified. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the dynamical modeling, analysis, and control of a spherical rolling robot. The rolling mechanism consists of a 2-DOF pendulum located inside a spherical shell with freedom to rotate about the transverse and longitudinal axis. The kinematics of the model has been investigated through the classical methods with rotation matrices. Dynamic modeling of the system is based on the Euler–Lagrange formalism. Nonholonomic and highly nonlinear equations of motion have then been decomposed into two simpler subsystems through the decoupled dynamics approach. A feedback linearization loop with fuzzy controllers has been designed for the control of the decoupled dynamics. Rolling of the controlled mechanism over linear and curvilinear trajectories has been simulated by using the proposed decoupled dynamical model and feedback controllers. Analysis of radius of curvature over curvilinear trajectories has also been investigated. [ABSTRACT FROM PUBLISHER]

The global citrus industry is continually confronted by new technological challenges to meet the ever-increasing consumer awareness and demand for quality-assured fruit. To face these challenges, recent trend in agribusiness is declining reliance on subjective assessment of quality and increasing adoption of objective, quantitative and non-destructive techniques of quality assessment. Non-destructive instrument-based methods are preferred to destructive techniques because they allow the measurement and analysis of individual fruit, reduce waste and permit repeated measures on the same item over time. A wide range of objective instruments for sensing and measuring the quality attributes of fresh produce have been reported. Among non-destructive quality assessment techniques, near-infrared (NIR) spectroscopy (NIRS) is arguably the most advanced with regard to instrumentation, applications, accessories and chemometric software packages. This paper reviews research progress on NIRS applications in internal and external quality measurement of citrus fruit, including the selection of NIR characteristics for spectra capture, analysis and interpretation. A brief overview on the fundamental theory, history, chemometrics of NIRS including spectral pre-processing methods, model calibration, validation and robustness is included. Finally, future prospects for NIRS-based imaging systems such as multispectral and hyperspectral imaging as well as optical coherence tomography as potential non-destructive techniques for citrus quality assessment are explored. [ABSTRACT FROM AUTHOR]

Over the last decade, the poultry sector has sought to develop ways to monitor chicken embryonic development as to optimize the incubation conditions. One of the parameters of development which may change under different incubation conditions is the angiogenesis in the chorioallantoic membrane (CAM). To be able to quantify these changes in the angiogenesis and detect long-term effects on health, a non-destructive technique is necessary. In this article, the first steps toward such a non-destructive technique are successfully taken. A spatially resolved spectroscopy set-up is built and tested for its potential to measure changes in angiogenesis with incubation time, and differences between a normal and hypercapnic incubation. In this first study, reflectance measurements are performed directly on the CAM as the eggshell considerably complicates the analysis. This issue should be addressed in future research to come to a really non-destructive technique. An experiment was conducted in which one group was incubated under normal conditions, and another under early prenatal hypercapnic conditions (i.e., increased CO2 concentrations). The angiogenesis in the CAM was measured at embryonic day (ED) 10, 13, and 16. The measurements showed a clear blood spectrum with an increasing amount of blood in time, and significant differences in the reflectance as function of the source-detector distances. However, no significant differences between the hypercapnia and the control group could be detected. © 2011 American Institute of Chemical Engineers Biotechnol. Prog.,, 2011 [ABSTRACT FROM AUTHOR]

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry–Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction. [ABSTRACT FROM AUTHOR]

As plants would benefit from adjusting and optimizing their architecture to changing environmental stimuli, ensuring a strong and healthy plant, it was hypothesized that different soil moisture levels would affect xylem and collenchyma development in basil (Ocimum basilicum L. cv. Marian) stems. Four different irrigation set-points (20, 30, 40 and 50% VWC), corresponding respectively to pF values of 1.95, 1.65, 1.30 and 1.15, were applied. Basil plants grown near the theoretical wilting point (pF 2) had a higher xylem vessel frequency and lower mean vessel diameter, promoting water transport under drought conditions. Cultivation at low soil moisture also impacted the formation of collenchyma in the apical stem segments, providing mechanical and structural support to these fast-growing stems and vascular tissues. The proportion of collenchyma area was significantly lower for the pF1.15 treatment (9.25 ± 3.24%) compared to the pF1.95 and pF1.30 treatments (16.04 ± 1.83% and 13.28 ± 1.38%, respectively). Higher fractions of collenchyma resulted in a higher mechanical stem strength against bending. Additionally, tracheids acted as the major support tissues in the basal stem segments. These results confirm that the available soil moisture impacts mechanical stem strength and overall plant quality of basil plants by impacting xylem and collenchyma development during cultivation, ensuring sufficient mechanical support to the fast-growing stem and to the protection of the vascular tissues. To our knowledge, this study is the first to compare the mechanical and anatomical characteristics of plant stems cultivated at different soil moisture levels. [ABSTRACT FROM AUTHOR]

Stomata, the microscopic pores surrounded by a pair of guard cells on the surfaces of leaves and stems, play an essential role in regulating the gas exchange between a plant and the surrounding atmosphere. Stomatal development and opening are significantly influenced by environmental conditions, both in the short and long term. The rapid rate of current climate change has been affecting stomatal responses, as a new balance between photosynthesis and water-use efficiency has to be found. Understanding the mechanisms involved in stomatal regulation and adjustment provides us with new insights into the ability of stomata to process information and evolve over time. In this review, we summarize the recent advances in research on the underlying mechanisms of the interaction between environmental factors and stomatal development and opening. Specific emphasis is placed on the environmental factors including light, CO2 concentration, ambient temperature, and relative humidity, as these factors play a significant role in understanding the impact of global climate change on plant development. [ABSTRACT FROM AUTHOR]

Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method. [ABSTRACT FROM AUTHOR]