Detecting early mealybug infestation stages on tomato plants using optical spectroscopy

Mealybugs (Hemiptera: Pseudococcidae) are important pests in agricultural and ornamental crops, including the tomato. Damage by mealybugs is characterized by a reduction in plant photosynthesis and growth due to sap feeding and also, as a result of honeydew excretion, from sooty mould development and virus transmission. The effectiveness of mealybug control strategies, including the application of insecticides and biological control, depends on the ability to detect the infestation at an early stage. Monitoring by visual observation is not very effective and is time-consuming. Optical spectroscopy represents a potential tool for detecting plant biotic stresses, including that caused by insect pests. In this study, we tested the feasibility of using optical spectroscopy for the early detection of mealybug infestation of tomato plants. An experiment was carried out using potted plants under field conditions, with 15 replicates per treatment and a randomised design. Two treatments were considered: 1) infested plants inoculated with three mealybug egg masses; and 2) control plants without mealybugs. The distance between pots was kept at 80 cm and the plants were frequently inspected to ensure control plants were not infested with mealybugs. The following parameters were recorded weekly over 5 weeks for each plant: 1) reflectance of marked leaves was measured with a USB4000 spectrometer across the wavelength 400-1,000 nm; 2) plant height; 3) leaf size; 4) mealybug density; and 5) presence and density of other pests. Results of principal component analysis (PCA) second derivative of the leaf reflectance showed a clear distinction between control and infested plants and a separation of components in the near infrared (NIR) region on the last day of the analysis (57 days). The reduction in absorption in the NIR region may be due to an increase in the quantity of air spaces within the leaf's mesophyll, changing the spatial distribution of the leaves' refractive index and, as a consequence, the light scattering contribution to the reflectance spectra. When tracking the evolution of the leaves' absorbance, infested leaves relative to control leaves had a tendency over time to have reduced absorbance in photosystem II and NIR plateau wavelengths. The evolution over time of the reflectance of analysed leaves at each wavelength fitted a quadratic curve, the coefficients of which discriminated between infested and control plants. This methodology has the potential to provide an objective measure of the degree of infestation by pests and the potential impact on the crop.FCT - Foundation for Science and Technology, PortugalCentre for Electronics, Optoelectronics and Telecommunications (CEOT) [UID/Multi/00631/2013

Preliminary Results on the Non-Destructive Determination of Pear (Pyrus communis L.) cv. Rocha Ripeness by Visible/Near Infrared Reflectance Spectroscopy

Pear (Pyrus communis L.), cv. Rocha was rapidly adopted by consumers due to its inherent quality and currently has great acceptance in both national and international markets, being mainly produced in the west region of Portugal. We report here a first approach to the use of the non-intrusive method of Visible/Near Infrared Reflectance Spectroscopy (Vis/NIRS) to estimate the ripeness of pear cv. Rocha. Mature unripe pears obtained from Frutoeste (Mafra, Portugal) after a six-month cold-storage, were maintained in a dark room at circa 20 degrees C during three weeks. They were followed using the Vis/NIRS in the wavelength band between 400 and 950 nm with two different configurations for the spectra acquisition, namely the Integrating Sphere (IS) and the Partial Transmittance (PT). The diffuse reflectance spectra obtained by the two configurations were compared with the respective fruit ripening parameters (colour, firmness, soluble solids content and % dry matter), determined through the standard techniques. Concerning the rough estimation of ripening parameters, data suggested an increase in both the intensity in the green to red band and pulp %dry matter, but a decreasing firmness. All other parameters remained constant. Relatively to the optical results, we have observed that the PT spectra exhibited clearer features than the IS spectra, especially from 700 nm onwards. This is probably due to the fact that the PT configuration probes more deeply into the fruit pulp. Three peaks at 600 (circa 30%), 725 and 812 nm (both at circa 50%) and a minimum at 675 nm, were identified in both IS and PT spectra. The values of reflectance peaks were approximately constant during ripening, but they moved to slightly lower wavelengths in the second week. A significant increase (circa 3-fold) in the minimal diffuse reflectance was observed in the second week, most probably associated partially, to a decrease in the fruit peel chlorophyll content

Estimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecture

Spectra-based methods are becoming increasingly important in Precision Agriculture as they offer non-destructive, quick tools for measuring the quality of produce. This study introduces a novel approach for esti-mating the soluble solids content (SSC) of 'Rocha' pears using the SpectraNet-32 deep learning architecture, which operates on 1D fruit spectra in the visible to near-infrared region (Vis-NIRS). This method was also able to estimate fruit temperatures, which improved the SSC prediction performance. The dataset consisted of 3300 spectra from 1650 'Rocha' pears collected from local markets over several weeks during the 2010 and 2011 seasons, which had varying edaphoclimatic conditions. Two types of partial least squares (PLS) feature selection methods, under various configurations, were applied to the input spectra to identify the most significant wavelengths for training SpectraNet-32. The model's robustness was also compared to a similar state-of-the-art deep learning architecture, DeepSpectra, as well as four other classical machine learning algorithms: PLS, multiple linear regression (MLR), support vector machine (SVM), and multi-layer perceptron (MLP). In total, 23 different experimental method configurations were assessed, with 150 neural networks each. SpectraNet-32 consistently outperformed other methods in several metrics. On average, it was 6.1% better than PLS in terms of the root mean square error of prediction (RMSEP, 1.08 vs. 1.15%), 7.7% better in prediction gain (PG, 1.67 vs. 1.55), 3.6% better in the coefficient of determination (R2, 0.58 vs. 0.56) and 5.8% better in the coefficient of variation (CV%, 8.35 vs. 8.86).info:eu-repo/semantics/publishedVersio

Non-destructive follow-up of ‘Jintao’ kiwifruit ripening through VIS-NIR spectroscopy – individual vs. average calibration model’s predictions

Visible/near infrared spectroscopy (Vis-NIRS) was used to monitor the yellow-fleshed kiwifruit (Actinidia chinensis Planch 'Jintao') ripening on two selected orchards along 13 weeks, from pre-harvest to the late harvest. Calibration models for several Internal Quality Attibutes (IQA) were built from the spectral data of 375 individual kiwifruit. The analyzed IQA were L*, a* and b* from the CIELAB color space, hue angle, chroma, firmness, dry matter (DM), soluble solids content (SSC), juice pH and titratable acidity (TA). Different pre-processing methods were tested for the construction of PLS calibration models. SSC and Hue were the best performing models with a correlation coefficient of 0.81 and 0.88, and root mean square error of prediction (RMSEP) of 1.27% and 1.95 degrees, respectively. The interpretation of the models in terms of the known absorption bands and the impact of signal to noise ratio (SNR) in them is discussed. The calibration models were used to perform average predictions of the IQA on orchard subareas, for each day of the experiment. These average predictions were compared with the IQA's average reference values on the same subareas and days. The model's metrics improved significantly through the averaging procedure, with RMSEP = 0.26-0.36% and R-2 = 0.99 for SSC; and RMSEP = 0.42 degrees - 0.56 degrees and R-2 = 1 for Hue. Since orchard management is done essentially through averages and not individual values, this result reinforces the applicability of the NIR technology for follow-up of fruit ripening in the tree.info:eu-repo/semantics/publishedVersio

Chlorophyll a Fluorescence: a Fast and Low-Cost Tool to Detect Superficial Scald in 'Rocha' Pear (Pyrus communis L. 'Rocha')?

This study aimed to test whether the chlorophyll a (Chla) fluorescence determined by a low-cost non-modulated fluorometer could provide fast, reliable and non-invasive estimators of superficial scald in 'Rocha' pear (Pyrus communis L. 'Rocha'). Fruit were harvested before the optimal maturation stage and cold stored under normal atmosphere for 7 months (NA: 0 degrees C, 90-95% RH) and 2 in controlled atmosphere (CA: 0 degrees C, 90-95% RH, 1.5 kPaO(2) + 0.5 kPa CO2) (T), or harvested at the optimal maturation stage and cold stored for 9 months under CA (C). Then, they were transferred to shelf-life conditions (22+/-2 degrees C, 70% RH) and followed for 7 d. Chla fluorescence, scald index (SI), ripening attributes, alpha-farnesene, conjugated trienols, and photosynthetic pigments were determined for each pear in both groups. Conditions chosen before shelf-life did not prevent the subsequent ripening of any fruit, but changed dramatically the superficial scald development pattern: in C fruit, the disorder developed progressively during shelf-life, whereas in T fruit, it peaked during storage. C fruit exhibited a significant negative correlation (R=-0.65; p<0.05) between Fv/Fm and scald development, but not with ripening (R=-0.15; p<0.05). As expected, the opposite was observed in T fruit, in which only a low, positive, yet significant correlation was found between Fv/Fm and ripening (R=0.44; p<0.05). The multiple regression approach using Fv/Fm and other Chla fluorescence parameters produced an equation from which we calculated the 'predicted' scald index in C fruit. This correlated clearly (R=0.73; p<0.05) with the real values visually assessed. If color values a*, b* and Hue were included in this multiple regression, the correlation was significantly enhanced (0.91; p<0.05). Although preliminary, this study has shown that basic Chla fluorescence parameters are valuable estimators of superficial scald in 'Rocha' pear and might be used in the early detection of the disorder

Flashing LEDs for microalgal production

Flashing lights are next-generation tools to mitigate light attenuation and increase the photosynthetic efficiency of microalgal cultivation systems illuminated by light-emitting diodes (LEDs). Optimal flashing light conditions depend on the reaction kinetics and properties of the linear electron transfer chain, energy dissipation, and storage mechanisms of a phototroph. In particular, extremely short and intense light flashes potentially mitigate light attenuation in photobioreactors without impairing photosynthesis. Intelligently controlling flashing light units and selecting electronic components can maximize light emission and energy efficiency. We discuss the biological, physical, and technical properties of flashing lights for algal production. We combine recent findings about photosynthetic pathways, self-shading in photobioreactors, and developments in solid-state technology towards the biotechnological application of LEDs to microalgal production.Foundation for Science and Technology (FCT, Portugal) [CCMAR/Multi/04326/2013]Nord UniversityNordland County Government (project Bioteknologi en framtidsrettet naering)INTERREG V-A Espana-Portugal project [0055 ALGARED + 5E]Portuguese Foundation for Science and Technology [SFRH/BD/105541/2014, SFRH/BD/115325/2016]info:eu-repo/semantics/publishedVersio

SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy

This work presents a new deep learning architecture, SpectraNet-53, for quantitative analysis of fruit spectra, optimized for predicting Soluble Solids Content (SSC, in Brix). The novelty of this approach resides in being an architecture trainable on a very small dataset, while keeping a performance level on-par or above Partial Least Squares (PLS), a time-proven machine learning method in the field of spectroscopy. SpectraNet-53 performance is assessed by determining the SSC of 616 Citrus sinensi L. Osbeck 'Newhall' oranges, from two Algarve (Portugal) orchards, spanning two consecutive years, and under different edaphoclimatic conditions. This dataset consists of short-wave near-infrared spectroscopic (SW-NIRS) data, and was acquired with a portable spectrometer, in the visible to near infrared region, on-tree and without temperature equalization. SpectraNet-53 results are compared to a similar state-of-the-art architecture, DeepSpectra, as well as PLS, and thoroughly assessed on 15 internal validation sets (where the training and test data were sampled from the same orchard or year) and on 28 external validation sets (training/test data sampled from different orchards/years). SpectraNet-53 was able to achieve better performance than DeepSpectra and PLS in several metrics, and is especially robust to training overfit. For external validation results, on average, SpectraNet-53 was 3.1% better than PLS on RMSEP (1.16 vs. 1.20 Brix), 11.6% better in SDR (1.22 vs. 1.10), and 28.0% better in R2 (0.40 vs. 0.31).project NIBAP ALG-01-0247-FEDER-037303, project OtiCalFrut ALG-010247-FEDER-033652info:eu-repo/semantics/publishedVersio

Difusao de ondas electromagneticas em plasmas turbulentos e coloidais

In this thesis we present a study on scattering of electromagnetic waves by electron density fluctuations in a plasma. Our work is divided in two main parts: scattering by turbulent density fluctuations and scattering in dusty plasmas. In what concerns scattering by turbulent fluctuations, we have made the theoretical study of the correlation between intensities of fields scattered at different regions of the plasma. Our results show that correlation experiments can improve the performance of collective light scattering diagnostics. We have also investigated the scaterring of light by a drift vortex street. Our results show that the vortex street is equivalent to a diffraction grating, and that the scattered light has intensity peaks given by a law similar to the Bragg law. In the second part of this thesis we addressed the problem of scattering and scattering by the Debye shielding cloud around the charged dust (Debye scattering). In the former case we have investigated the effect of dust charge fluctuation and collisions between dusts and plasma particles and in the latter we have studied the effect of the high number of charges around the dust grainAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1249-074 Lisboa, Portugal / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga

Simultaneous determination of the mean and standard deviation of quasi-monodisperse size distributions of microspheres by static light scattering

The objective of this work is to quantify the accuracy and precision of particle sizing performed through light scattering goniometry. A rigorous characterization of this simple method depends on a complete description of the scattering geometry, and to our knowledge this has not yet been done. We have determined the central diameter (d0) and standard deviation (σ) of samples of polystyrene spheres with a narrow normal distribution of diameters through a χ2 fit to the light scattering phase function. The fit is performed simultaneously in the variables d0 and σ. The model for the fit includes Mie scattering calculations, integration of the phase function over a normal distribution of diameters and a complete description of the scattering geometry. Experimental tests on spheres with diameters between 2 and 7 μm show that this method provides excellent accuracy and precision for the determination of d0 and good accuracy for the determination of σ. The precision in σ is poor in relative terms but in absolute terms it is around 0.05 μm within the range of sizes tested. We calculate the uncertainty limits for the determination of d0 and σ and show that they are consistent with all the supplier values except the value of σ for the 2 μm spheres, where aggregation was detected by optical microscopy. Other topics included in this work are: (i) comparison between our method and the classical simple fit through a pure monodisperse system (σ = 0); and (ii) dependence of the quality of sizing on the angular range of the measurement. Finally, comparison with published results shows that simple goniometry may outperform more complex methods

On the application of spatially resolved reflectance and diffuse light backscattering goniometry to the prediction of firmness in apple ‘bravo de esmolfe’

Proceedings of the International Conference “Environmentally friendly and safe technologies for quality of fruit and vegetables”, held in Universidade do Algarve, Faro, Portugal, on January 14-16, 2009. This Conference was a join activity with COST Action 924.In this study we have made exploratory tests on a set of 40 apples (Malus domestica Borkh.) ‘Bravo de Esmolfe’, using spatially resolved reflectance (SRR) and diffuse light backscattering goniometry (DLBG). The objective was to test the potential of DLBG for firmness prediction, as compared with SRR, whose potential has been already proved in the literature. SRR is performed with a red diode laser and a CMOS camera. DLBG uses the same laser shining on the apple and a photomultiplier tube collecting the light reemitted from a small area, at angles ranging from 90 deg (tangent to the surface) to 180 deg (normal to the surface). From the measurements several parameters have been calculated (e.g. decay exponent for SRR profiles, anisotropy factor for the DLBG angular distributions) and Partial Least squares (PLS) models for the prediction of firmness were build. The model based on DLBG variables (only) and on SRR variables (only) gave similar results. From here we conclude that, within the obvious statistical limitations of the test, DLBG seems to match the potential of SRR for firmness prediction. The possibility of combining both measures in one model is also discussed