Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

[EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS).Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221113Martelli, G. P., Boscia, D., Porcelli, F., & Saponari, M. (2015). The olive quick decline syndrome in south-east Italy: a threatening phytosanitary emergency. European Journal of Plant Pathology, 144(2), 235-243. doi:10.1007/s10658-015-0784-7Olmo, D., Nieto, A., Adrover, F., Urbano, A., Beidas, O., Juan, A., … Landa, B. B. (2017). First Detection of Xylella fastidiosa Infecting Cherry (Prunus avium) and Polygala myrtifolia Plants, in Mallorca Island, Spain. Plant Disease, 101(10), 1820-1820. doi:10.1094/pdis-04-17-0590-pdnSaponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D., & Saldarelli, P. (2019). Xylella fastidiosa in Olive in Apulia: Where We Stand. Phytopathology®, 109(2), 175-186. doi:10.1094/phyto-08-18-0319-fiVergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., … Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00666Thenkabail, P. S., & Lyon, J. G. (Eds.). (2016). Hyperspectral Remote Sensing of Vegetation. doi:10.1201/b11222Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231-245. doi:10.1016/j.rse.2013.07.031Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sensing, 7(10), 13586-13605. doi:10.3390/rs71013586Hernández-Clemente, R., Navarro-Cerrillo, R., Ramírez, F., Hornero, A., & Zarco-Tejada, P. (2014). A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing, 6(11), 11627-11648. doi:10.3390/rs61111627Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1). doi:10.1038/s41438-018-0043-0Ma, Q., Su, Y., Luo, L., Li, L., Kelly, M., & Guo, Q. (2018). Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310. doi:10.1016/j.ecolind.2018.07.050Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi:10.1109/jstars.2017.2711482Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., … Dandekar, A. M. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1), 1-25. doi:10.1007/s13593-014-0246-1Calderón, R., Navas-Cortés, J., & Zarco-Tejada, P. (2015). Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sensing, 7(5), 5584-5610. doi:10.3390/rs70505584Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., … Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 432-439. doi:10.1038/s41477-018-0189-7Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing, 10(7), 1091. doi:10.3390/rs10071091Vicent, A., & Blasco, J. (2017). When prevention fails. Towards more efficient strategies for plant disease eradication. New Phytologist, 214(3), 905-908. doi:10.1111/nph.14555Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1). doi:10.1186/s13007-018-0324-5Bourgeon, M. A., Gée, C., Debuisson, S., Villette, S., Jones, G., & Paoli, J. N. (2016). « On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices. Precision Agriculture, 18(3), 293-308. doi:10.1007/s11119-016-9489-yUnderwood, J. P., Hung, C., Whelan, B., & Sukkarieh, S. (2016). Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture, 130, 83-96. doi:10.1016/j.compag.2016.09.014Zampetti, E., Papa, P., Di Flaviano, F., Paciucci, L., Petracchini, F., Pirrone, N., … Macagnano, A. (2017). Remotely Controlled Terrestrial Vehicle Integrated Sensory System for Environmental Monitoring. Sensors, 338-343. doi:10.1007/978-3-319-55077-0_43Hiremath, S. A., van der Heijden, G. W. A. M., van Evert, F. K., Stein, A., & ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50. doi:10.1016/j.compag.2013.10.005Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., … Agüera, J. (2015). Highlights and preliminary results for autonomous crop protection. 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Scientific Reports, 7(1). doi:10.1038/s41598-017-17957-

Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

[EN] In polyethylene terephthalate's (PET)'s recycling processes, separation from polyvinyl chloride (PVC) is of prior relevance due to its toxicity, which degrades the final quality of recycled PET. Moreover, the potential presence of some polymers in mixed plastics (such as PVC in PET) is a key aspect for the use of recycled plastic in products such as medical equipment, toys, or food packaging. Many works have dealt with plastic classification by hyperspectral imaging, although only some of them have been directly focused on PET sorting and very few on its separation from PVC. These works use different classification models and preprocessing techniques and show their performance for the problem at hand. However, still, there is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method, when using NIR hyperspectral images. There is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method when using near-infrared hyperspectral images.Universitat Politecnica de Valencia, Grant/Award Number: UPV-FE-16-B18This research was partially supported by the Universitat Politècnica de València under the project UPV‐FE‐16‐B18.Galdón-Navarro, B.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2018). Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images. Journal of Chemometrics. 32(1):1-14. https://doi.org/10.1002/cem.2980S11432

In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties

[EN] One of the most studied techniques for the non-destructive determination of the internal quality of fruits has been visible and nearinfrared (VIS-NIR) reflectance spectroscopy. This work evaluates a new non-destructive in-line VIS-NIR spectroscopy prototype for in-line identification of five apple varieties, with the advantage that it allows the spectra to be captured with the probe at the same distance from all the fruits regardless of their size. The prototype was tested using varieties with a similar appearance by acquiring the diffuse reflectance spectrum of the fruits travelling on the conveyor belt at a speed of 0.81 m/s which is nearly 1 fruit/s. Principal component analysis (PCA) was used to determine the variables that explain the most variance in the spectra. Seven principal components were then used to perform linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA was found to be the best in-line classification method, achieving 98% and 85% success rates for red and yellow apple varieties, respectively. The results indicated that the in-line application of VIS-NIR spectroscopy that was developed is potentially feasible for the detection of apple varieties with an accuracy that is similar to or better than a laboratory system.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by INIA and FEDER funds through project RTA2015-00078-00-00. Victoria Cortes Lopez thanks the Spanish Ministry of Education, Culture and Sports for FPU grant (FPU13/04202).Cortes-Lopez, V.; Cubero-García, S.; Blasco Ivars, J.; Aleixos Borrás, MN.; Talens Oliag, P. (2019). In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties. Food and Bioprocess Technology. 12(6):1021-1030. https://doi.org/10.1007/s11947-019-02268-0S10211030126Aleixandre-Tudo, J. 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E., Wubbels, A., van der Sluis, C., & van der Meer, J. M. (2006). Understanding factors affecting near infrared analysis of potato constituents. Journal of Near Infrared Spectroscopy, 14(1), 27–35.He, Y., Li, X., & Shao, Y. (2007). Fast discrimination of apple varieties using Vis/NIR spectroscopy. International Journal of Food Properties, 10(1), 9–18.Hernández, A., He, Y., & García, A. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87(3), 303–313.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: with applications in R. New York: springer.Jie, D., Xie, L., Rao, X., & Ying, Y. (2014). Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biology and Technology, 90, 1–6.Kader, A. A., Kasmire, R. F., Mitchell, F. G., Reid, M. S., Sommer, N. F., & Thompson, J. F. (1985). Postharvest technology of horticultural crops (Special publication, mum. 3311, p. 192). Davis: Cooperative Extension, University of California.Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88(7), 1115–1127.Lammertyn, J., De Baerdemaeker, J., & Nicolaï, B. (2000). Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology, 18(2), 121–132.Liu, F., Jiang, Y., & He, Y. (2009). 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Classification of selected apple fruit varieties using Naive Bayes. Indian Journal of Computer Science and Engineering, 7(1), 13–19.Sabanci, K., & Ünlersen, M. F. (2016). Different apple varieties classification using kNN and MLP algorithms. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 166–169.Sádecká, J., Jakubíková, M., Májek, P., & Kleinová, A. (2016). Classification of plum spirit drinks by synchronous fluorescence spectroscopy. Food Chemistry, 196, 783–790.Salguero-Chaparro, L., Baeten, V., Abbas, O., & Peña-Rodríguez, F. (2012). On-line analysis of intact olive fruits by vis-NIR spectroscopy: optimisation of the acquisition parameters. Journal of Food Engineering, 112(3), 152–157.Santos, P., Santos, F., Santos, J., & Bezerra, H. (2013). Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. 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Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine

[EN] Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450¿1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits.This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. The authors wish to thank Fruits de Ponent (Lleida) for providing the fruit.Munera-Picazo, S.; Amigo, JM.; Aleixos Borrás, MN.; Talens Oliag, P.; Cubero-García, S.; Blasco Ivars, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control. 86:1-10. https://doi.org/10.1016/j.foodcont.2017.10.037S1108

Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

[EN] Background: The increasing demand for quality assurance in agro-food production requires sophisticated analytical methods for in-line quality control. One of these techniques is visible and near-infrared (VIS-NIR) spectroscopy, which has low running costs, does not need sample preparation, and is non-destructive, environmentally friendly, and fast. Despite these advantages, only a limited amount of research has been conducted on VIS-NIR in-line applications to measure, control, and predict quality in fruits and vegetables. Scope and approach: The applicability of VIS-NIR spectroscopy for the off-line and in-line monitoring of quality in postharvest products has been addressed in this review. The document focuses on the comparison between the two processes for the same agro-food product, highlighting the main advantages and disadvantages, problems, solutions, and differences. Key findings and conclusions: VIS-NIR techniques, combined with chemometric methods, have shown great potential due to their fast detection speed, and the possibility of simultaneously predicting multiple quality parameters or distinguishing between products according to the objectives. Being able to automate processes is a great advantage compared to routine off-line analyses, mainly due to the savings achieved in time, material, and personnel. However, in numerous cases, in-line implementation has not been accomplished in the corresponding studies, hence the scarcity of real in-line applications. Recent demands, together with the advances being made in the technology and a reduction in the price of equipment, makes VIS-NIR technology an analytical alternative for continuous real-time food quality controls, which will become predominant in the next few years.This work was partially funded by INIA and FEDER funds through research project RTA2015-00078-00-00.Victoria Cortés López thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202).Cortes-Lopez, V.; Blasco Ivars, J.; Aleixos Borrás, MN.; Cubero-García, S.; Talens Oliag, P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology. 85:138-148. https://doi.org/10.1016/j.tifs.2019.01.015S1381488