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- Estimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecturePublication . Martins, J. A; Rodrigues, Daniela; Cavaco, A. M.; Antunes, Maria Dulce; Guerra, Rui Manuel Farinha das NevesSpectra-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).
- Spectral analysis, biocompounds, and physiological assessment of Cork Oak leaves: unveiling the interaction with Phytophthora cinnamomi and beyondPublication . Guerra, Rui; Pires, Rosa; Brazio, António; Cavaco, Ana Margarida; Schütz, Gabriela; Coelho, Ana CristinaThe cork oak tree (Quercus suber L.) symbolizes the Montado landscape in Portugal and is a central element in the country’s social and economic history. In recent decades, the loss of thousands of cork oaks has been reported, revealing the ongoing decline of these agroforestry ecosystems. This emblematic tree of the Mediterranean Basin is host to the soil-born root pathogen Phytophthora cinnamomi, an active cork oak decline driver. In this framework, the early diagnosis of trees infected by the oomycete by non-invasive methods should contribute to the sustainable management of cork oak ecosystems, which motivated this work. Gas exchange and visible/near-infrared (400–1100 nm) reflectance spectroscopy measurements were conducted on leaves of both control and P. cinnamomi inoculated plants. These measurements were taken at 63, 78, 91, 126, and 248 days after inoculation. Additionally, at the end of the experiment, biochemical assays of pigments, sugars, and starch were performed. The spectroscopic measurements proved effective in distinguishing between control and inoculated plants, while the standard gas exchange and biochemistry data did not exhibit clear differences between the groups. The spectral data were examined both daily and globally, utilizing the PARAFAC method applied to a three-way array of samples × wavelengths × days. The separation of the two plant groups was attributed to variations in water content (4v (O−H)); shifts in the spectra red edge; and structural modifications in the epidermal layer and leaves’ mesophyll. These spectral signatures can assist in the field identification of cork oaks that are interacting with P. cinnamomi.
- Spatiotemporal modelling of the quality and ripening of two cultivars of "Algarve Citrus" orchards at different edaphoclimatic conditionsPublication . Cavaco, Ana M.; Cruz, Sandra P.; Antunes, M. Dulce; Guerra, Rui; Pires, Rosa; Afonso, Andreia M.; Brazio, António; Silva, Leonardo; Lucas, Marcia Rosendo; Daniel, Mariana; Panagopoulos, ThomasAlgarve Citrus are non-climacteric Protected Geographical Indication (PGI) commodities. They are harvested with minimal levels of juice content (>35 %), soluble solids content (SSC) (>10 %) and maturation index (MI) (>8), as required by the respective PGI normative reference. These internal quality attributes (IQA) are usually determined in small samples of fruit collected from the orchards close to harvest. This study aimed to use geostatistics to help predict the optimal harvest date (OHD) of two sweet orange (Citrus sinensis (L.) Osbeck) cultivars, namely, 'Newhall', and 'Valencia Late', at two different edaphoclimatic conditions observed in the locations of Quarteira, at the coast, and Paderne, near a mountainous area. Two orchards of 0.5-0.7 ha per cultivar were chosen and a total of 25 trees were georeferenced within each orchard, comprising 100 sampling points/trees. Firmness, juice content, SSC and MI of fruit were determined through time. In general, the fruit grown in Quarteira showed higher SSC and MI and lower firmness values, ripening two months earlier than those grown in Paderne, although the full effect of the various edaphoclimatic factors on these results are not fully understood. However, geospatial modelling of ripening has shown a large variability within the orchards, with some IQA evolution patterns observed in some orchards and/or cultivars but not in the others. Specifically, 1) a negative correlation between the firmness and MI spatial patterns; 2) a variable decay rate of firmness, much faster in Paderne for 'Valencia Late'; 3) local minima in juice content, below 35 %, observed in restricted spatial areas and in specific time periods, and which were clearer in 'Newhall'. These local variations highlight the need for an optimized management based on geospatial modelling. For example, the variable decay rate of firmness must be taken into account during fruit harvest and postharvest handling. On the other side, the observation of localized plots with juice content below 35 % must be contextualized in the broader picture of the entire orchard which, in the present study, always had consistent temporal average level above 35 %. This study has provided evidence that fruit ripening variability should be considered in the site-specific orchard management of citrus to optimize their harvest date.
- Ripening assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration modelsPublication . Pires, Rosa; Guerra, Rui Manuel Farinha das Neves; Cruz, Sandra; Antunes, MDC; Brazio, António; Afonso, Andreia M.; Daniel, Mariana; Panagopoulos, Thomas; Gonçalves, Isabel; Cavaco, Ana M.The aim of this study was the non-destructive assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) ripening, based on the prediction of internal quality attributes (IQA) by short-wave near-infrared reflectance spectroscopy (SW-NIRS) calibration models. Spectra from fruit of 50 trees located in two different orchards, were acquired on tree using a customized portable visible near-infrared (vis-NIR) system. Partial least squares (PLS) was used to build the various IQA calibration models. The models were tested through internal validation (IV) and external validation (EV). Generally, the IV results were always superior to those of EV: regarding IV, a high regression coefficient (R2) and low root mean square error of prediction (RMSEP) were achieved, revealing a good predictive performance for juice pH (R2 = 0.80; RMSEP = 0.10; SDR = 2.23), soluble solids content (SSC) (R2 = 0.79; RMSEP = 0.75 %; SDR = 2.27), titratable acidity (TA) (R2 = 0.73; RMSEP = 0.24 % citric acid; SDR = 1.94) and the maturation index (MI) (R2 = 0.80; RMSEP = 1.38; SDR = 2.2). The best EV predictions were obtained for TA (R2 = 0.69; RMSEP = 0.38 % citric acid; SDR = 1.24), and MI (R2 = 0.69; RMSEP = 2.07; SDR = 1.49). Calibration models for glucose, fructose and sucrose showed medium-coarse predictions for both validation strategies. A detailed investigation of MI models was performed, to understand the causes of their poor EV results. In the context of EV, model updating strategies were explored by using some validation samples to improve the calibration model. The methods of bias correction and spiking were tested, showing a clear improvement in the predictions.
- Feature discovery in NIR spectroscopy based Rocha pear classificationPublication . Daniel, Mariana; Guerra, Rui Manuel Farinha das Neves; Brazio, António; Rodrigues, Daniela; M. Cavaco, A.; Antunes, Maria Dulce; Valente de Oliveira, JOSÉNon-invasive techniques for automatic fruit classification are gaining importance in the global agro-industry as they allow for optimizing harvesting, storage, management, and distribution decisions. Visible, near infra-red (NIR) diffuse reflectance spectroscopy is one of the most employed techniques in such fruit classification. Typically, after the acquisition of a fruit reflectance spectrum the wavelength domain signal is preprocessed and a classifier is designed. Up to now, little or no work considered the problem of feature generation and selection of the reflectance spectrum. This work aims at filling this gap, by exploiting a feature engineering phase before the classifier. The usual approach where the classifier is fed directly with the reflectances measured at each wavelength is contrasted with the proposed division of the spectra into bands and their characterization in wavelength, frequency, and wavelength-frequency domains. Feature selection is also applied for optimizing efficiency, predictive accuracy, and for mitigating over-training. A total of 3050 Rocha pear samples from different origins and harvest years are considered. Statistical tests of hypotheses on classification results of soluble solids content - a predictor of both fruit sweetness and ripeness - show that the proposed preliminary phase of feature engineering outperforms the usual direct approach both in terms of accuracy and in the number of necessary features. Moreover, the method allows for the identification of features that are physical chemistry meaningful.
- Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculturePublication . Cavaco, A. M.; Utkin, Andrei B.; Marques da Silva, Jorge; Guerra, RuiAs a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
- Non-destructive follow-up of ‘Jintao’ kiwifruit ripening through VIS-NIR spectroscopy – individual vs. average calibration model’s predictionsPublication . Afonso, Andreia M.; Antunes, Maria Dulce; Cruz, Sandra; Cavaco, A. M.; Guerra, Rui Manuel Farinha das NevesVisible/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.
- Editorial: recent advancements on the development and ripening of Mediterranean fruits and tree cropsPublication . El-kereamy, Ashraf; Caruso, Marco; Torres, Carolina A.; Ana, CavacoThe Mediterranean basin and other Mediterranean-type ecosystems (MTE) are home to many tree crops domesticated and adapted well to their environment. Several of them present specific development and ripening traits that challenge established models. Climate changes that are occurring in the Mediterranean area and in other MTE tends to aggravate the already irregular rainfall and temperature patterns, posing detrimental outcomes on crop performance, productivity, and changes in fruit ripening. With these climate changes, one would expect changes in the fruits and tree crops components growing in these ecosystems. Currently, we are experiencing a tremendous advance in the technology that allows researchers to study in-depth the basic phenomenon and find significant novel data to establish guidelines for new cultural practices, breeding programs, and variety selection that can better adapt to the changing conditions. The goal of this Research Topic was to highlight recent studies on the anatomical, physiological, metabolomic, and genomic processes occurring throughout the development and ripening of fruits and tree crops grown in the Mediterranean Basin and MTE, from field until postharvest. Since many of them are perennial species, they are subjected to adverse environmental conditions throughout their entire life cycle. Thus, the effect of cultural practices, varying environmental factors, as well as the impact of the various stresses on the performance of these tree crops were also acknowledged.