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Puffiness is one of the dominant postharvest disorders in easy-peeling citrus cultivars. In this study, the structural changes between healthy and puffy Satsuma mandarin were investigated and the potential of using optical methods for disorder detection was explored. To gain more insight in this disorder, the external appearance and internal quality attributes were first compared between healthy and puffy Iwasaki Satsuma mandarins at three harvest times. Although no consistent differences were observed in the appearance of fruits, the soluble solids content and Brix minus acid values in puffy mandarin were found to be higher compared to the corresponding healthy fruit. The structural properties of the flavedo and albedo tissue layer in the peel were quantified from X-ray CT scans. Whilst no differences were observed in the size of the oil glands in the flavedo, the pore size in the albedo of puffy mandarin was found to be larger with later harvest. The bulk optical properties of the intact fruit were estimated from laser scatter images with a metamodel calibrated on optical phantoms. The reduced scattering coefficient (μ s ') for the intact fruit was found to be lower in puffy mandarin relative to healthy fruit. The distinction between healthy and puffy mandarin based on μ s ' was further validated on Goku Wase Satsuma mandarin. The results obtained indicate that healthy and puffy mandarin can be separated well based on their μ s ' at all the selected wavelengths. This provides a basis for the non-destructive optical detection of puffing disorder at an early stage. [Display omitted] • External and internal attributes were compared between healthy and puffy mandarin. • Thickness of both flavedo and albedo tissue layers was lower in puffy mandarin. • Pore size in the albedo of puffy mandarin was larger at a later harvest time. • The μ s ' of the intact fruit was lower in the puffy than the healthy mandarin. [ABSTRACT FROM AUTHOR]

Fibrous forage and biomass crops such as grass and maize are used at large scale as feed for ruminants and feedstock for biogas production. These crops are typically harvested by chopping them in small particles which are light and suitable for partial pneumatic conveying, where the particle trajectory is determined by their aerodynamic properties. While the aerodynamic properties of many grains have been studied, no reports were found for perennial ryegrass. Chopped ryegrass samples of leaf blades and stems were collected in the fields. After imaging the fresh harvested particles to quantify their size and surface area, their terminal velocity was measured with the fluidised-bed method. The relationship between the mass, projected area and moisture content of the stems and blades with their terminal velocities were modelled. In line with theory, mass and moisture content was positively correlated with terminal velocity, while projected area was negatively correlated with it. Smaller terminal velocities were observed for the blades (1.75 m s−1) compared to the stems (4.44 m s−1). The drag coefficients of grass particles were calculated, and for blades it was negatively correlated with the particle Reynold's number lying within Newton's regime (103 < Re < 3 × 105), whilst no significant correlation was found for stems. The average drag coefficient of blades (0.75) was larger than stems (0.54). No significant effect (p-value <0.01) of moisture content on the drag coefficient of grass particles was observed. This confirms that the drag coefficient is a shape parameter, related to formation of airstreams around the particle. • Aerodynamic properties of ryegrass were quantified in a wind tunnel. • The terminal velocity of grass blades was 61% smaller than that of stems. • Terminal velocities can be predicted from mass, area and moisture content (R2 = 0.77). • Grass particles' drag coefficient was calculated and compared to similar materials. • The drag coefficient of grass blades was 39% larger than that of stems. [ABSTRACT FROM AUTHOR]

[Display omitted] • The principles of four methods for food quality analysis are discussed. • Different MVDA methods are illustrated on a common data set as an example-based review. • The most suitable MVDA method depends on the nature of the data set and the objective of the research. • The potential of more advanced multivariate methods should be further explored. These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods. [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]

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]

Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks. The problem addressed in this work is the efficient pre-processing, selection, and ordering of data blocks for targeted applications in SO-PLS. We introduce the PROSAC–SO–PLS methodology, which employs pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC). This approach identifies the best pre-processed data blocks and their sequential order for specific SO-PLS applications. The method uses a stepwise forward selection strategy, facilitated by the rapid Gram-Schmidt process, to prioritize blocks based on their effectiveness in minimizing prediction error, as indicated by the lowest prediction residuals. To validate the efficacy of our approach, we showcase the outcomes of three empirical near-infrared (NIR) datasets. Comparative analyses were performed against partial-least-squares (PLS) regressions on single-block pre-processed datasets and a methodology relying solely on PROSAC. The PROSAC–SO–PLS approach consistently outperformed these methods, yielding significantly lower prediction errors. This has been evidenced by a reduction in the root-mean-squared error of prediction (RMSEP) ranging from 5 to 25 % across seven out of the eight response variables analyzed. The PROSAC–SO–PLS methodology offers a versatile and efficient technique for ensemble pre-processing in NIR data modeling. It enables the use of SO-PLS minimizing concerns about pre-processing sequence or block order and effectively manages a large number of data blocks. This innovation significantly streamlines the data pre-processing and model-building processes, enhancing the accuracy and efficiency of chemometric models. Summary of the PROSAC–SO–PLS methodology applied to one of the response variables (protein) of the miniS-milk dataset, which is one of the three datasets presented in this study. In this specific case, multiple blocks of spectral data derived from multiple miniature spectrometers measuring the same milk samples simultaneously are subjected to an ensemble of pre-processing techniques within the PROSAC framework. The five most variance-explaining blocks identified by PROSAC are subsequently utilized as input variables for SO-PLS, facilitating the construction of a model that optimizes block utilization for predicting the protein content in milk, with protein content reference values obtained from laboratory analyses. [Display omitted] • PROSAC and SO-PLS are combined for improved multi-block data fusion. • PROSAC streamlines SO-PLS by easing pre-processing and block order concerns. • PROSAC–SO–PLS effectively manages a large number of input data blocks. • PROSAC–SO–PLS is tested on three NIR datasets, surpassing existing state-of-art. • PROSAC–SO–PLS reduces prediction errors by 5–25 % for the evaluated NIR datasets. [ABSTRACT FROM AUTHOR]

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]

• Reflectance, Transmittance, Fluorescence, Raman and integrated imaging is covered. • Instrumentation is discussed with special attention for real-time imaging. • The major steps in hyperspectral data analysis are discussed. • Overview of applications in external and internal quality evaluation. • Review of applications in food safety detection. In the past 20 years, hyperspectral imaging has been widely investigated as an emerging, promising technology for evaluating quality and safety of horticultural products. This technology has originated from remote sensing and joins the domains of machine vision and point spectroscopy to provide superior image segmentation for the detection of defects and contaminations, and to map the chemical composition. Thanks to the advancements in instrumentation and data analysis in the past two decades, hyperspectral imaging technology has evolved into a powerful nondestructive inspection tool and the scope of applications in postharvest quality and safety evaluation has expanded tremendously. In this article, different imaging modes (reflectance, transmittance, fluorescence and Raman) and their combinations, and the potential for real-time acquisition of hyperspectral images at industry relevant speeds are first discussed in terms of their advantages and disadvantages. Next reviewed are different data processing/analysis methods and associated steps from data pre-processing over the spectral and spatial domains to the actual model building and performance evaluation. An overview is then given of hyperspectral imaging applications for external quality and defect evaluation, internal quality and maturity assessment, and food safety detection of horticultural products. Finally, a brief discussion is presented on the challenges and opportunities in future development and application of hyperspectral imaging technology in food quality and safety evaluation of horticultural products. [ABSTRACT FROM AUTHOR]

In the past decade, hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods for non-destructive food quality analysis and control. This paper demonstrates fast and automatic quantification of food ingredients with hyperspectral imaging in the visible and near-infrared range in combination with chemometrics and image processing techniques. The application of ingredient quantification in bread flour recipes requires high spatial resolution in addition to spectral discrimination power. Our results show that automatic and accurate quantification of all ingredients can be done, reaching pixel discrimination accuracies above 90% and ingredient quantification errors within the required 1% absolute error in weight. The classification accuracy obtained using 15 wavebands on the test images is around 15% higher than what was obtained with colour imaging. • Accurate ingredient discrimination (>90%) with VNIR hyperspectral imaging. • Accurate seed count based on spatial post-processing of classified image. • Accurate quantification of weight in non-seed ingredients. • Increased discrimination accuracy versus color imaging. [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]

Incorporating monitoring and forecasting technologies has the potential to enhance the different aspects of winemaking. By monitoring certain chemical parameters, it is possible to evaluate the progress of the fermentation and phenolic extraction, while also detecting deviations from expected behaviour. This study aims to investigate the development of a rapid forecasting method and different strategies to improve the accuracy of process monitoring. The first part involves the introduction of partial least squares (PLS) calibration models able to forecast the trajectories of phenolic extraction during fermentation using grape extract spectra. Additionally, novel methods for reoptimizing calibration models are proposed, with the intention of increasing the prediction accuracy. Whilst this method is transferable to PLS models, principal component regression (PCR) models were used instead as a more simplistic way to test the new methods. First, dynamic PCR linear regression coefficients were adapted over time to capture the compound evolution in the wine matrix. Secondly, PCR calibration models were reoptimized using interpolated spectral and reference data. The forecasting PLS calibration models have shown promising results for total anthocyanin content (mg/L), colour density, polymeric pigments (mg/L), MCP tannins (mg/L), and total phenolic index with overall R2 values on the validation set above 0.6, and low RSMEV values. Using interpolated data to re-optimise models was found to be more effective than global models or those using the calculated regression coefficients methods for predicting the concentration of a particular compound at a certain time point. While R2 values are suboptimal for all considered PCR models, in 72% of the cases analysed, the interpolation method for re-optimisation showed RMSEV values between 6% and 83% lower than those reported for a more traditional PCR model and other re-optimisation strategies. Interpolated models have shown consistency in predicting values, along with good performance metrics, indicating that they could be applied to samples for which calibration models do not exist. • This study uses grape extracts spectral data to predict wine phenolic content. • Time-specific calibration models were optimised to increase prediction accuracy. • Acceptable R2 and RMSEP for phenolic prediction in fermentation were obtained. • Using interpolated data to re-optimise models was found to be effective. [ABSTRACT FROM AUTHOR]

• A fiber-optic spatially-resolved spectroscopy (SRS) setup measured milk NIR spectra. • The setup evaluated 30 different illumination-to-detection distances (1.1–4 mm) • Shorter illumination-to-detection distances were optimal for lactose prediction. • Larger illumination-to-detection distances were optimal for fat and protein. • The accuracy of the composition predictions are well within the ICAR requirements. To support in-line quality control of raw milk and ensure the close monitoring of the metabolic and udder health of dairy cows, we propose a fiber-optic spatially-resolved spectroscopy (SRS) setup. This setup allows to vary the interaction of long-wave near-infrared (LW-NIR) light with the milk by submerging a separate optical illumination and detection fiber into the sample and altering their relative distance to optimize measurements for specific milk component(s). We evaluated this approach for predicting milk fat, protein, and lactose content and determined the optimal illumination-to-detection distances for each milk component. The region between 1.1 and 1.8 mm was optimal for lactose, and between 2.2 and 3.8 mm for fat and protein. These distance ranges resulted in a root-mean-square error of prediction (RMSEP) of less than 0.10 % weight/weight (wt/wt) for milk fat, and lower than 0.13 % (wt/wt) for protein and lactose. Integration of these distances into a fiber-optic SRS reflectance probe would allow to simultaneously determine the fat, protein, and lactose content of raw milk in-line with high accuracy. [ABSTRACT FROM AUTHOR]

In the search for a healthy indoor climate, chemical cleaning agents are replaced by bacillus strains to create an indoor ecosystem in which these harmless bacteria clean the surfaces by consuming the organic molecules in dust and dirt. To optimise these ecosystems, it is important to monitor the bacterial growth and activity, but the reference methods are time consuming and involve a serious time delay. Therefore, the potential of fluorescence spectroscopy for monitoring the activity of bacillus strains was investigated in this study. We confirmed that fluorescence signals could be detected for Bacillus vegetative cells at a load above 108 cells mL−1. As the fluorescence emission was mostly in the 330–400 nm range after excitation in the 280–290 nm range, we could attribute this signal to the tryptophan residues. The Bacillus fluorescence signals could be distinguished from those released by pure l -tryptophan, plate count agar medium, and Bacillus spores, which were classified with 100% accuracy by PLSDA. In comparison to other microbes, bacillus strains, Escherichia coli (MG1655) and Saccharomyces cerevisiae were found to produce an indistinguishable peak at 340 nm in response to excitation at 280 nm. However, using a hybrid multivariate classifier involving PCA combined with a One-Class SVM on the area-normalised fluorescence emission spectra in response to excitation at 280 nm allowed footprints of Bacillus cells to be identified with a classification accuracy of 78%. • Bacillus subtilis can be detected at above 108 cell mL-1. • Fluorescence of bacillus cells is mainly generated by tryptophan residues. • A hybrid multivariate classifier can discriminate Bacillus strains from other microbes. [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]

Inspired by the rapid progress and existing limitations in surface plasmon resonance (SPR) biosensing technology, we have summarized the recent trends in the fields of both chip-SPR and fiber optic (FO)-SPR biosensors during the past five years, primarily regarding smart layers design, multiplexing, continuous monitoring and in vivo sensing. Versatile surface chemistries, biomaterials and nanomaterials have been utilized thus far to generate smart layers on SPR platforms and as such achieve oriented immobilization of bioreceptors, improved fouling resistance and sensitivity enhancement, collectively aiming to improve the biosensing performance. Furthermore, often driven by the desires for time- and cost-effective quantification of multiple targets in a single measurement, efforts have been made to implement multiplex bioassays on SPR platforms. While this aspect largely remains difficult to attain, numerous alternative strategies arose for obtaining parallel analysis of multiple analytes in one single device. Additionally, one of the upcoming challenges in this field will be to succeed in using SPR platforms for continuous measurements and in vivo sensing, and as such match up other biosensing platforms where these goals have been already conquered. Overall, this review will give insight into multiple possibilities that have become available over the years for boosting the performance of SPR biosensors. However, because combining them all into one optimal sensor is practically not feasible, the final application needs to be considered while designing an SPR biosensor, as this will determine the requirements of the bioassay and will thus help in selecting the essential elements from the recent progress made in SPR sensing. Image 1 • Continuous progress in material sciences promotes SPR biosensors development. • Smart layers improve receptor orientation, fouling resistance and sensitivity. • Challenging multiplexing is complemented by parallel analysis on SPR biosensors. • Continuous and in vivo sensing with SPR biosensors remains yet to be attained. [ABSTRACT FROM AUTHOR]

Banana and its dried products are one of the most common foods consumed all over the world. Quality attributes of dried banana slices are typically evaluated using time-consuming and labor intensive methods which do not allow to evaluate each individual slice. Replacement of the current destructive methods for quality evaluation of dried banana slices by fast, non-destructive methods would provide large added value to the food industry. Therefore, the aim of this study was to monitor the moisture content, texture and color of banana slices during the drying process in a fast and non-destructive way. VNIR hyperspectral reflectance imaging in the 400–1000 nm range was selected for this purpose. Thanks to a cross-polarized configuration the effects of glare or specular reflection on the banana slice surfaces in the hyperspectral diffuse reflectance images was largely reduced. Reference quality attributes for all the slices (moisture content, texture and color ( L*, a*, b* values) obtained using conventional destructive methods and colorimeter were predicted from their corresponding average reflectance spectra extracted from the hyperspectral images by means of partial least squares regression (PLSR). The PLSR calibration models were validated on samples which had not been used for model calibration. The results were very good for water content ( R 2 P  = 0.97, RMSEP  = 0.05 kg water/kg DM), quite good for and b* value ( R 2 P  = 0.83, RMSEP  = 1.95), and reasonable for texture ( R 2 P  = 0.66, RMSEP  = 11.8 N), a* value ( R 2 P  = 0.53, RMSEP  = 1.32) and L* value ( R 2 P  = 0.61, RMSEP  = 5.92). Subsequently, these calibration models were used to predict those quality attributes at pixel level for the validation slices to visualize the spatial distribution of these quality parameters at different stages during the drying process. The obtained results clearly indicate the potential of cross-polarized hyperspectral reflectance imaging for non-destructive monitoring of the quality attributes of banana slices during drying. [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]

Insight in the transmission of light through intact fruit is important to optimize and interpret the signals acquired with spectroscopy methods such as tunable diode laser absorption spectroscopy (TDLAS). To this end, we quantified the bulk optical properties (BOP) of skin, outer cortex, inner cortex, and core tissue of three pear (Pyrus communis) cultivars (Thimo , Cepuna , and Conference) using double integrating spheres measurements in combination with the inverse adding doubling method. Similar trends and values were observed for the bulk optical properties of the three cultivars. A clear chlorophyll-a absorption feature was present in the bulk absorption coefficient (μ a) at 690 nm for the skin samples. At 761 nm, the bulk scattering coefficient (μ s) decreased from the skin towards the core region, for example from 256 ± 34 cm−1 to 146 ± 22 cm−1 for Conference pears. The observed anisotropy factor (g) of the skin was lower than these of the other tissue layers (0.87 ± 0.026 vs. 0.94 ± 0.08 for Conference). Next, Monte Carlo voxelized media (MCVM) simulations were performed based on these BOP values and validated against measurements with a TDLAS setup for gas in scattering media absorption spectroscopy (GASMAS) at 761 nm. In these experiments, the sample thickness was reduced by sequentially slicing the pear from the skin towards the center. Light transmittance through an intact pear at the height of the core region was found to be very low (1.9 × 10−4 ± 8.5 × 10−5%) and exponentially increased around 100-fold when the sample thickness decreased from 64 mm to 24 mm. The simulated transmittance profiles based on the measured BOP values at 761 nm followed the same trend as the measured GASMAS transmittance as a function of the fruit thickness. This shows that simulations based on these measured BOP values can be used to optimize the measurement configuration for O 2 monitoring in intact pear fruit with GASMAS. • The bulk optical properties of pear tissue layers: skin, outer cortex, inner cortex, and core. • 3D Monte Carlo simulation of diffuse transmittance through an intact pear. • Comparison of simulated and measured diffuse transmittance vs pear sample thicknesses. • Simulations provided a valuable tool for optimizing the amplification of GASMAS detectors in diffuse transmittance mode. [ABSTRACT FROM AUTHOR]

Driven by increasing food demand and the urgent need for intelligent land use, the size and complexity of agricultural machinery has increased significantly. Monitoring the operation of machines during long working days is a very challenging task for the operators. Automatic monitoring systems can lighten the load and also pave the way towards fully autonomous machines. This paper proposes an automatic condition monitoring system for detecting operation anomalies on agricultural machine components with reciprocating motion, and applies it to a use-case on a combine harvester header cutter bar. Cyclostationary analysis techniques are employed to develop filtering algorithms to extract informative features, which are monitored through the implementation of statistical process control (SPC) using control charts. Together with a comparison of several filtering and feature extraction techniques, an analysis is provided on the influence of sensor type and position on anomaly detection performance. Filtering benefit was found to be highly dependent on the considered sensor type and its location, with increases in Matthews correlation coefficient (MCC) ranging from 0 to 50%, resulting in maximal MCC values of 1. Suitable feature calculation resulted in average prediction performance improvements over 10% in MCC values for nearly all considered sensor types and their locations. These results highlight the importance of intelligent sensor selection for condition monitoring purposes on agricultural machinery and the added value of SPC involving cyclostationary analysis techniques for anomaly detection. • Knife impacts are detected with a Matthews correlation coefficient of 0.995. • Sensors mounted closer to the cutter bar performed best. • The proposed synchronous filters clearly improved performance. • The standard deviation per rotation captured the signal response during impact well. [ABSTRACT FROM AUTHOR]

Ethiopia is well known for its use of an ard plough dating from antiquity – maresha – which fractures and disturbs the soil. However, hardly any notable progress of experimental research on this animal drawn tillage tool in the field has been made. The attendant problems in current practise are soil- maresha interaction, viz., uneven oxen strength along with different pace of walking, uncontrolled implement behaviour, and field conditions. Taking stock of the experimental research on animal drawn tillage tools in general, most of the documented works on the dynamics of the interaction between soil and animal drawn tillage tools tend to rely on trial-and-error based on factors mainly based on experience and cultural context. As such, no research tailored to systematically handle the link between maresha plough and soil bin experiments exists. To this aim, this study developed a mobile in-situ soil bin facility in which the system was calibrated, tested, and evaluated under outdoor experimental conditions, wherein online measurements of draught, speed, and depth of tillage were carried out. The insights and observations gained from the experimentation were discussed and reported in terms of smooth run, overload, cyclic forces, zero speed with minimal force, stoppage, speed measurement with no force, force measurement with no speed, and low speed with low force. [ABSTRACT FROM AUTHOR]

Ethiopian farmers have been using an ox-drawn breaking plough, known as ard plough – maresha, for thousands of years. Maresha is a pointed, steel-tipped tine attached to a draught pole at an adjustable shallow angle. It has narrow side-wings, attached to the left and right side of it, to push soil to either side without inverting. The aim of this paper is to explore the effect of side-wings on draught using a field soil bin test facility. To this end, a mobile and an in-situ soil bin test system, for online measurements of draught, was designed and developed. This research considered tool geometry ( maresha plough with and without side-wings) and rake angle (shallow – 8°, medium deep – 15°, and deep – 24°, representing primary, secondary and tertiary tillage processes in Ethiopia, respectively). Maresha plough with side-wings has greater contact area, between the moving soil and tool, than its wingless counterpart. When the ploughshare surface and soil slide relative to one another, the draught expected to increase with contact area, as adhesion and friction resistance increases with area. However, experimental analysis indicated that the maresha with side-wings required less draught compared to maresha without side-wings ( ρ < 0.001) . This might be attributed to the effect of side-wings on crack propagation by a wedging effect to enhance and facilitate subsequent ploughing. This paper also dealt with the effect of rake angle on draught. Though the depth setup was getting smaller d 1 < d 2 < d 3 for the successive tillage runs, analysis showed increment in draught force ( ρ < 0.001) with rake angle. This might be attributed to higher soil compaction that comes with depth and downward force resulting from repeated use of maresha every season to the same depth for thousand years. Although more and rigorous studies should be undertaken considering soil, tool, and operational parameters to arrive at conclusive results, this paper gave some insights regarding effect of side-wings on maresha plough and rake angle on draught. This shows that there is still room for improvement of maresha plough geometry for minimum draught requirement and optimum soil manipulation. [ABSTRACT FROM AUTHOR]

'Braeburn' apples are susceptible to internal browning disorders when stored under controlled atmosphere (CA) conditions with unfavorable gas compositions. The progression of CA-related disorders in apple tissues is dynamic, noting a decrease in porosity during early storage due to cellular breakdown and pore flooding, and an increase in porosity in later stages due to structural collapse and cavity formation. Utilizing grating-based X-ray dark-field radiography, which leverages X-ray small-angle scattering to detect microstructural changes below the pixel scale, this study assesses the technique's efficacy in identifying internal disorders in 'Braeburn' apples at both early and later stages. A machine learning approach was applied to compare the diagnostic capabilities of dark-field imaging with those of X-ray absorption radiography at identical image resolutions. Results indicate that for early-stage disordered fruit detection, X-ray dark field radiography is 10 % more accurate than absorption radiography, regardless of the machine learning classifiers that were applied. In the later stage of browning, dark-field imaging performs similarly to absorption imaging. High-resolution micro-computed tomography scans suggested that the distinct detection performance of dark-field imaging may be attributed to the more pronounced microstructural differences between healthy and early-stage defective tissues than those between healthy and later-stage defective tissues. The insights from this work will guide the application of X-ray dark-field systems in fruit quality assurance, particularly in detecting internal disorders. • X-ray dark-field radiography is more suited for detecting low-porosity defects. • X-ray absorption radiography is more effective for identifying high-porosity defects. • Changes in X-ray dark-field image are closely linked with microstructural changes. [ABSTRACT FROM AUTHOR]

Optimization of controlled atmosphere (CA) storage of pome fruit crucially depends on understanding of respiratory gas exchange. Different fruit properties are involved in describing gas transfer and respiration kinetics, which can vary between cultivars, fruit batches and individual fruit. Determination of these properties currently requires a combination of different time-consuming techniques and data analysis procedures. This study introduces a more efficient single method for O 2 gas exchange characterization of fruit based on the non-destructive pathlength-resolved GASMAS (gas in scattering media absorption spectroscopy) technique. GASMAS was used to measure the absolute intercellular O 2 concentration in fruit of different apple cultivars (Malus x domestica Borkh.) with distinct porosity including 'Jonagold', 'Braeburn', and 'Nicoter'. To resolve the ambiguity between variation in pathlength and O 2 concentration inherent to GASMAS, the pathlength travelled through pores cavities was quantified through a second GASMAS measurement at the absorption line of H 2 O vapour. Dynamic experiments were then performed in which the fruit was subjected to varying external O 2 concentrations while measuring the internal O 2 response. The observed dynamic profiles of external and internal O 2 concentration were fitted with a lumped -respiration model to estimate the effective mass transfer coefficient, maximum respiration rate and Michaelis Menten constant of intact fruit. The model showed a good fit (R 2 >0.95) with the measured data and reliable estimates for the model parameters. The proposed method provides a novel and more efficient way to estimate the gas exchange properties of fruit that are essential for optimizing CA storage conditions. Future challenges for the proposed method are with applications to other gasses such as CO 2 and ethylene. • Non-destructive and quantitative internal O 2 measurement of apple using GASMAS. • GASMAS measurement on an intact apple in diffuse transmittance mode. • Fast determination of O 2 exchange and respiration kinetic parameters of apple. [ABSTRACT FROM AUTHOR]

Calibration transfer has been traditionally performed in the context of transferring models between instruments using standard samples. Recently, new methodologies and applications have shown that transfer techniques can be adopted to achieve calibration transfer between other types of domains, such as product form, variant or seasonality. In addition, to achieving a higher efficiency for calibration transfer, it is desirable to perform the transfer without the need for standard samples or new reference analyses. Therefore, we propose a method for unsupervised calibration transfer based on the orthogonalization for structural differences between domains. The method has been successfully applied to one simulated dataset and two real datasets. In the studied cases, the proposed methodology allowed to achieve a successful transfer of calibration models and enabled the interpretation of the interferences responsible for the degradation of the original calibration models when transferred to the new domain. [Display omitted] [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]

[Display omitted] • The analytical method for calculating the THz parameters of thin films is inadequate. • A numerical method to extract the THz optical parameters is elaborated. • Applications to water and hydrogel thin films in a "cuvette" set-up is presented. • THz-TDS gives insights into the hydration state of agarose hydrogels. • Codes used in this work are made freely available to promote the use of THz-TDS. Terahertz time-domain spectroscopy (THz-TDS) is an emerging optical technique that has potential applications in the characterization of (bio)materials. However, the complicated extraction of optical parameters from multi-layered and optically thin samples is a barrier towards its acceptance by applied scientists. Therefore, the aim of this work is to provide a straightforward approach for the extraction of the THz absorption coefficient and index of refraction profiles of aqueous thin films in a window-sample-window configuration, which is ubiquitous in many laboratories (i.e., sample in a cuvette). A numerical approach-based methodology that accounts for multiple layers, Fabry–Pérot effect, and sample thickness is elaborated which involves an optical interference model based on a tri-layer structure and a simple thickness estimation technique. This method was validated on water samples where a good agreement was found with the THz optical parameters of water reported in the literature, while the use of a commercial software resulted in erroneous optical parameters estimates when used without due regard to its limitations. A case study was then performed to demonstrate the ability of the proposed method to characterize agarose hydrogels with varying degree of sulfation. It was demonstrated that THz-TDS can provide insight into the hydration state of the agarose hydrogels, including the relative number of the hydrogen bonds between the hydroxyl moieties of water and the polysaccharide network which is perturbed by the presence of sulfate. The trend in the index of refraction profiles suggested microstructural differences between the agarose hydrogels, which were confirmed by visualizing the agarose network morphology using cryo-SEM imaging. [ABSTRACT FROM AUTHOR]

[Display omitted] • Milk composition • Calibration maintenance using unsupervised bias monitoring • Near-infrared spectroscopy • Partial least squares • Bulk milk analysis Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples. In this study, we propose and validate a method to monitor the bias drift and two methods to tackle it. The first method requires no extra measurements and uses a modified version of Partial Least Squares Regression to estimate and correct the bias. This method is based on the assumption that the mean concentration of the predicted component remains constant over time. The second method uses regular bulk milk measurements as a reference for bias correction. This method compares the measured concentrations of the bulk milk to the volume-weighted average concentrations of individual milk samples predicted by the sensor. Any difference between the actual and calculated bulk milk composition is then used to perform a bias correction on the predictions by the sensor system. The effectiveness of these methods to improve the component prediction was evaluated on data originating from a custom-built sensor that automatically measures the NIR reflectance and transmittance spectra of raw milk on the farm. We evaluate the practical use case where models for predicting the milk composition are trained upon installation of the sensor at the farm, and later used to predict the composition of subsequent samples over a period of more than 6 months. The effectiveness of the fully unsupervised method was confirmed when the mean concentration of the milk samples remained constant, while the effectiveness reduced when this was not the case. The bulk milk correction method was effective when all relevant samples for the component were measured by the sensor and included in the analyzed bulk milk, but is less effective when samples included in the bulk which are not measured by the sensor system. When the necessary conditions are met, these methods can be used to extend the lifetime of deployed prediction models by significantly reducing the bias on the predicted values. [ABSTRACT FROM AUTHOR]

• Spectral imaging proves effective in discriminating kernels from material other than kernel in freshly harvested maize forage. • A limited number of appropriate spectra can provide significant improvements in the detection of kernels in maize forage. • A Faster R-CNN model for multispectral images showed improved results compared to its performance on RGB images with the same resolution. In this study, the use of hyperspectral imaging was examined to enhance the kernel processing assessment of maize forage, which is crucial for optimizing the production of dairy cow feed and biogas production. As the visual contrast between the kernels and other crop particles is quite limited, spectral imaging could provide better kernel classification. A pushbroom hyperspectral imaging system was used, scanning samples collected during maize harvesting in the 400–1000 nm range. A PLSDA model for pixel classification was developed to distinguish kernel spectra from the other particles in the forage, achieving a pixel-level classification accuracy of 95.2 %. Next, the most important wavelengths were identified by means of a stepwise procedure using the Wilks Lambda criterion. A Pixel classification using the top five discriminating wavelengths achieved nearly the same accuracy as the full spectrum wavelength model (95.2 % compared to 93.5 %) and did considerably better than the RGB classifier's 86.3 % accuracy. Finally, these top 5 discriminating wavebands were applied in an object detection deep learning model, more specifically a Faster R-CNN model. While object detection based on these 5 wavebands stilled outperformed object detection based on RGB wavebands, detection performance, as measured by AP50, was rather low. This weak performance resulted from the low resolution of the hyperspectral imaging camera. [ABSTRACT FROM AUTHOR]

• Bulk optical properties of tissue layers were measured for Satsuma mandarin. • The pigment related μ a and the μ s in juice vesicles increased along harvest time. • Color change was presented in the μ a of flavedo along harvest time. • Influence from fruit size was related to the development stage. • The difference in the μ s of flavedo among cultivars enhanced during maturation. As Satsuma mandarin is a multi-layered fruit, the optical properties of the different tissue layers influence the performance of spectroscopy-based quality detection models. Therefore, the variation in the bulk optical properties (BOP) of the inner (juice vesicles) and outer (flavedo) tissue layer of Satsuma mandarin was investigated for different harvest times, fruit sizes and cultivars. Satsuma mandarin fruit from three different cultivars (Iwasaki, Okitsu and Goku Wase) were harvested at three different times around the recommended harvest date and the BOP of the inner and outer tissue layers were estimated from double integrating spheres measurements. Along the harvest time, the absorption peak related to carotenoids in the bulk absorption coefficient (μ a) spectrum increased in both tissue layers, while the values attributed to chlorophylls decreased in the flavedo. Moreover, a slightly increasing trend over the harvest time was observed in the bulk scattering coefficient (μ s) spectra of juice vesicles, especially at the early stage. The μ a values of both tissue layers and the μ s values of juice vesicles deviated among different fruit sizes, which can be related to the development stage. Smaller fruit presented a higher carotenoids concentration in juice vesicles, an advanced color change in flavedo throughout the maturation stages and higher μ s values in juice vesicles at earlier maturation stages. The μ a differences in pigment absorption among different cultivars were related to the maturation order. Earlier maturing cultivars possessed higher μ a values related to carotenoids absorption in juice vesicles and advanced color changes in flavedo. The μ s values of the flavedo differed among the cultivars and these differences were enhanced during maturation. These insights in the sources of variation in the bulk optical properties of Satsuma mandarin tissues can be used for simulation-based optimal design of an optical measurement configuration and interpretation of the acquired signals in spectroscopy-based quality detection and monitoring. [ABSTRACT FROM AUTHOR]

The sugar profile of a syrup determines its suitability for use in different food applications. The traditional wet chemical method for sugar profile analysis is time-consuming, destructive, and requires several chemical reagents. Therefore, this study aimed to develop a rapid method for determining the concentration of the main sugars (glucose, maltose, maltotriose, and fructose) in tapioca syrups using NIR spectroscopy in the 12000–4000 cm−1 wavenumber region or 800–2500 nm wavelength range. As large spectral variation due to water and liquid glucose was observed, orthogonal projection (OP) was evaluated to reduce those effects. Results showed that for all sugars studied, orthogonal projection prior to partial least squares regression (PLSR) improved the prediction performance in terms of the root mean square error of prediction (RMSEP) by 5–47% and the coefficient of determination for prediction (R p 2) by 3–7%. The developed models had high R p 2 and low RMSEP values, which provides opportunities for industrial monitoring of the sugar profiles of tapioca syrup products. • NIR spectroscopy allows quantifying many sugars in tapioca syrups. • Interference removal by orthogonal projection enhances prediction of syrup sugars. • High-accuracy prediction models for the individual sugars were built by using PLSR. • Identification of important wavelengths for syrup sugar profiling was established. [ABSTRACT FROM AUTHOR]

• Successful classification of flying insects from their optical signatures using machine learning and deep learning. • Practice oriented validation is key in developing insect classification models. • Random data splitting can lead to over-optimistic classification performance estimates. • Classification at the insect genus level is a more feasible option than on the species level. [ABSTRACT FROM AUTHOR]

Crop height and density estimation ahead of the combine harvester have been investigated over the last 30 years, but they remain a challenge. LiDAR technology is increasingly being investigated for phenotyping and monitoring of cereals. However, so far, little has been published about the influence of laser mounting position and robust online measurement of height and density from a single LiDAR scan. Therefore, the influence of the angle and height of the LiDAR mounting on crop height and density estimation in wheat and barley was investigated in this study. Tests were conducted in different crop heights, densities, moisture levels and varieties. The crop height was estimated with a root mean squared error of 0.082 m at an angle of 90° between the LiDAR scanning plane and the horizontal. Two methods were compared for crop density estimation. The inter-percentile and the transversal variance method both successfully predicted the crop density, but the variance based method performed better with a coefficient of determination (R2) of 0.77 and a root mean squared error of 82 g [dry ears] m-2. When considering only one variety, the performance improved to reach an R2 of 0.8 and a root mean squared error of 44 g [dry ears] m-2. Variation in mounting angle of the sensor had less effect on the prediction accuracy than the mounting height. • LiDAR allows to estimate crop height and density in small grain crop. • Crop height cannot be estimated properly at angles to the horizontal below 70°. • Variance based crop density estimation is robust against small viewing angle variations. • LiDAR data correlate better to ears dry mass than to number of tillers. [ABSTRACT FROM AUTHOR]