Abstract
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

Abstract
This scientific paper delves into the effects of water stress on grapevines, specifically focusing on gene expression and polyphenol production. We conducted a controlled greenhouse experiment with three hydric conditions and analyzed the expression of genes related to polyphenol biosynthesis. Our results revealed significant differences in the expression of ABCC1, a gene linked to anthocyanin metabolism, under different irrigation treatments. These findings highlight the importance of anthocyanins in grapevine responses to abiotic stresses. By integrating genomics, metabolomics, and systems biology, this study contributes to our understanding of grapevine physiology under water stress conditions and offers insights into developing sensor technologies for real-world applications in viticulture. © 2023 by the authors.

Abstract
The potential of hyperspectral UV–VIS–NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. © 2023 by the authors.

Abstract

Abstract
This paper introduces a tomography-like method for assessing grape maturation. It analyses inner tissue spectra through point-of-measurement (POM) sensing. A multi-block hierarchical principal component analysis (MHPCA) algorithm was used for the spectral reconstruction of total grapes (skin, pulp, and seed). Two grape cultivars, Loureiro (white; n = 216) and Vinhao (red; n = 205) were measured at 12 dates after veraison (DAV). The reconstructed spectra showed no significant differences (p < 0.001) from the originals for both grapes. Loureiro had better statistical metrics (Person's correlation coefficient (r) values for: total grape: 0.99, skin: 1; pulp: 1, seed: 0.94) than Vinhao (r values for: total grape: 0.92, skin: 0.92; pulp: 0.95, seed: 0.95). Using self learning artificial intelligence (SL-AI), the following parameters were predicted for both grapes: soluble solids content (%; MAPE <13%), puncture force (N; MAPE <29%), chlorophyll content (a.u.; MAPE <29%), and anthocyanin content (a.u.; MAPE <17%, Vinhao only). When comparing observed values with predicted skin, pulp, and seed spectra, Vinhao showed no statistical differences for most parameters, except pulp chlorophyll on one DAV in the final maturation stage. The same was done with the Loureiro cultivar. Although Loureiro mostly showed no statistical differences in assessed parameters across tissues and dates, variations were found in pulp and skin chlorophyll content and puncture force. This tomography-like approach based on tissue maturation can help viticulturists to access instant data on grape maturation, supporting informed decision-making and promoting more sustainable agricultural practices.

Abstract
This study used a tomography-like analysis to reconstruct the hyperspectral data from different tissues of the grapes: skin, pulp, and seeds. The dataset included 216 grapes of Loureiro (VIVC 25085) and 205 Vinhão (VIVC 13100) at various dates from the veraison until the harvest. A more comprehensive spectral data analysis identified how the internal tissues are related to the total grape spectra. Each tissue was reconstructed separately by decomposing the whole grapevine hyperspectral information. The results showed that the spectral reconstruction was more successful for Loureiro than Vinhão, with a mean absolute error of 6.08% and 33.32%, respectively. Partial least squares (PLS) regression models were developed for both cultivars using the reconstructed spectral data, enabling the modelling of ºBrix, puncture force (N), chlorophyll (a.u.), and anthocyanin content (a.u.). These models exhibited strong performance, with R2 > 0.8 and mean absolute percentage errors (MAPE) below 37%. This study emphasises the critical role of considering the grape's internal tissue in assessing its maturation process. The findings introduce an innovative methodology for efficiently evaluating grape maturation dynamics and inner tissue characteristics. By highlighting the importance of internal tissue analysis, this research paves the way for expedited and accurate monitoring of grape maturation, offering valuable insights into physiological-based viticultural practices and grape quality assessment. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).