BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//CXFGDE BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-FSMFXW@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T100000 DTEND;TZID=GMT:20230613T101500 DESCRIPTION:The advent of satellite technologies has made it possible to ma ke georeferenced observations of the entire globe at periodic intervals of a few days and with high spatial resolutions.\nESA's Copernicus mission m akes available open-source data from the Sentinel-2 constellation created to provide useful information for agricultural purposes thanks to appropri ately calibrated multispectral images [2].\nThe NDVI (Normalized Vegetatio n Index) [1] can be correlated with some biophysical or agronomic variable s of the vineyard [3].\nThe work presents the results of a two-year work c arried out in the province of Turin in the Piedmont region\, that involved six vineyards cultivated with different varieties (Nebbiolo\, Erbaluce) a nd two vine training system (pergola and espalier). The NDVI georeferenced data were provided by the EOS Crop Monitoring web platform.\nThe experime ntal design divided the vineyards in three classes of vigor areas\, define d through a pre-survey operated by comparing the series of georeferenced N DVI images collected the summer before.\nIn the different vineyards for ea ch of the chosen vigor areas\, five plants were identified and used as a g round reference to evaluate a series of vegetative-productive parameters. The total amount of plants monitored were 30 for Nebbiolo and 55 for Erbal uce.\nAll NDVI index showed significant predictability for the studied var iables.\nAs expected\, the trend of the quantitative variables was positiv ely related to the NDVI while the qualitative variables were negatively re lated. As far as the percentage mean error was concerned a high predictabi lity\, (error 1÷7% respectively for Erbaluce and Nebbiolo vineyards). Con sidering the canopy architecture\, the leaf layers were accurately predict ed from the NDVI (R2 0\,72 and 0\,55 respectively for Erbaluce and Nebbiol o) with an error around 10%. Regarding the fruit compartment a strong diff erence emerged between the systems. The shaded cluster percentage in the N ebbiolo vines was highly predictable with (R2 0\,57\, error 6%). In Erbalu ce the error was higher (36%) with a correlation index R2 of 0\,42. This f act derives from the higher variability of the plants in the compared plot s. The number of clusters were predicted with a minor error in Nebbiolo th an in Erbaluce (9% and 29%\, R2 0\,70 and 0\,16 respectively) and for the bud fertility (8% and 15%\, R2 0\,83 and 0\,36 respectively). In sum\, the true productive traits appeared as the less predictable in the Erbaluce v ineyards\, with 31% error in yield (R2 0\,26) compared to a less erroneous prediction (error 22% and R2 0\,63) in Nebbiolo vines. The pruning wood weight was similarly predicted from the NDVI with 21 and 23% error\, with a correlation index R2 of 0\,41 and 0\,28 for Erbaluce and Nebbiolo respec tively.\nThe PCA analysis\, allowed discriminating observations based on v igor attributes and consistently with the measured variables\, even when a ll the observations\, for the different varietal combinations\, are proces sed simultaneously with the same multivariate model. \nThe study confirmed the possibility to use Sentinel-2 NDVI output to map the vineyards variab ility also in small plots (< 1 ha)\, estimating the vineyard canopy densit y\, the productive and wine most important technological parameters. \n\n\ n\n[1] Giovos\, R.\, Tassopoulos\, D.\, Kalivas\, D.\, Lougkos\, N.\, & Pr iovolou\, A. (2021). Remote sensing vegetation indices in viticulture: A c ritical review. Agriculture\, 11(5)\, 457.\n[2] Sarvia\, F.\, De Petris\, S.\, Orusa\, T.\, & Borgogno-Mondino\, E. (2021). MAIA S2 versus sentinel 2: spectral issues and their effects in the precision farming context. In Computational Science and Its Applications–ICCSA 2021: 21st Internationa l Conference\, Cagliari\, Italy\, September 13–16\, 2021\, Proceedings\, Part VII 21 (pp. 63-77). \n[3] Vélez\, S.\, Rançon\, F.\, Barajas\, E.\ , Brunel\, G.\, Rubio\, J. A.\, & Tisseyre\, B. (2022). Potential of funct ional analysis applied to Sentinel-2 time-series to assess relevant agrono mic parameters at the within-field level in viticulture. Computers and Ele ctronics in Agriculture\, 194\, 106726. DTSTAMP:20260512T203411Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Monitoring Erbaluce and Nebbiolo vineyards by means of Sentinel-2 N DVI index maps - Enrico Borgogno-Mondino\, Alberto Cugnetto\, Giorgio Maso ero\, Peppino Sarasso URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/FSMFXW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-UBQPWP@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T173000 DTEND;TZID=GMT:20230613T174500 DESCRIPTION:Foliar NIR Spectroscopy and EOS platform for monitoring polyphe nolic maturity in Nebbiolo\n\nA. Cugnetto1\, M. Altare2\, G. Masoero 1\,3\ , S. Guidoni 3\,1.\n\n1 Accademia di Agricoltura di Torino (TO)\n2 Az. Vit ivinicola Costa di Bussia\, Monforte (CN)\n3 Dipartimento Scienze Agrarie\ , Forestali e Alimentari\, Università di Torino (TO)\n\nA Nebbiolo vineya rd was divided into three vigor zones (High\, Medium\, Low) according to t he NDVI index survey supplied by EOS Crop Monitoring web platform. In fou r sessions\, leaf samples were collected on which petiolar pH [1] and the NIR spectrum were determined using the SCiOTM v 1.2 apparatus (740-1070 nm \, 331 reflectance points). From samples of 10 berries the seeds were clea ned and scanned by NIRS obtaining 99 spectra. The polyphenolic maturity of the seeds was expressed based on the Non-Extractable Polyphenols / Extrac table Polyphenols (PSM) ratio\, analyzed according to the Di Stefano metho d [2]. The value was estimated by a WinISI-II PLS equation recalculated on published data [3] which has a predictive value of R2 = 0.70 and RMSE err or = 8%. From the NIR spectra of 164 leaves a SPAD value was estimated (by unpublished equation) and the PSM of the seeds was regressed on the 16 co mposition parameters [4]. The most important variables that explain the mo del\, were those related to the bromatological composition of the vegetal wall (Cellulose\, ADL\, digestible-NDF\, non-digestible-NDF\, Total digest ibility). The fitting of the 10 vines vigor group gave an R2 = 0.88 (Mean RMSE 12%). The petiolar pH did not show significant relations with the see ds PSM. The direct calibration of the NIR spectrum on the seeds PSM made with the WinISI\, revealed an R2 = 0.84 (MRMSE 5%\, with an outlier group )\, while using the PLSR of LabSCiO we obtained R2=0.73 (MRMSE 6% with an outlier group).\nThis part of the work demonstrates that a proximal scruti ny of the NIR spectrum of Nebbiolo leaves allow an estimation of the matur ity of the seed polyphenols provided that the result is consolidated with the mean of at least 15 replicate measurements.\nOnce the individual calcu lations were examined\, the group averages were processed by performing a linear regression of the PSM on the averages of the available variables ex tracted from the NIR spectra\, and on the NDVI measurements taken from th e Sentinel-2 satellite. The examined variables had different importance an d the SPAD (R2=0.49) had the maximum one. The NDVI from satellite had fitt ed to the seeds PSM with R2 = 0.34\; it was under the forecast accuracy pr ovided by the leaves spectra set\, but is worthy of attention for the simp licity of use. The obtained linear equation was PSM = 5.71 + 2.42 * NDVI. \n\nThe work demonstrates that with the modern Satellite remote sensing te chnologies\, it is possible to improve the grape sampling during the matur ation period\, better identifying the internal plot variability\, that is related to different seed ripening levels. The leaf NIR spectra detected at ground level with SCIOTM v 1.2\, is a rapid proximal method for estimat ing the Nebbiolo seed ripening\, directly in the farm\n\n\n\n1 Masoero G\, Cugnetto A. 2018 The raw pH in plants: a multifaceted parameter. Journal of Agronomy Research\, 1: (2)\, 18-34. ISSN: 2639-3166. DOI10.14302/issn.2 639-3166.jar-18-2397. https://openaccesspub.org/jar/article/871\n2 Di Stef ano R\, Cravero MC. 1991 Metodi per lo studio dei polifenoli nell'uva. Riv . Vitic. Enol\, 2\, 37-45.\n3 Cugnetto A\, Masoero G. (2021) Colored anti- hail nets modify the ripening parameters of Nebbiolo (Vitis vinifera L.) a nd a smart NIRS can predict the polyphenol features. JAR 4 (1)\, 24-45. ht tps://openaccesspub.org/jar/article/1701\n4 Peiretti P G\, Masoero G and T assone S 2017: Comparison of the nutritive value and fatty acid profile of the green pruning residues of six grapevine (Vitis vinifera L.) cultivars . Livestock Research for Rural Development. Volume 29\, Article #194.Retri eved October 3\, 2017\, http://www.lrrd.org/lrrd29/10/pier29194.html DTSTAMP:20260512T203411Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Monitoring the seeds phenolic maturity in Nebbiolo vineyard by mean s of NDVI index vs foliar NIR spectroscopy - Alberto Cugnetto\, Matteo Alt are\, Giorgio Masoero\, Silvia Guidoni URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/UBQPWP/ END:VEVENT END:VCALENDAR