{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:34:17Z","timestamp":1772069657473,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T00:00:00Z","timestamp":1581465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004343","name":"Stavros Niarchos Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004343","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["732695"],"award-info":[{"award-number":["732695"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-destructive tools for the in situ evaluation of vine fruit quality and vineyard management can improve the market value of table grape. We proposed a new approach based on a portable fluorescence sensor to map the ripening level of Crimson Seedless table grape in five different plots in the East, Central-North and South of the Macedonia Region of Greece. The sensor provided indices of ripening and color such as SFRR and ANTHRG correlated to the chlorophyll and anthocyanin berry contents, respectively. The mean ANTHRG index was significantly different among all the plots examined due to the occurrence of different environmental conditions and\/or asynchronous ripening processes. The indices presented moderate, poor in some cases, spatial variability, probably due to a significant vine-to-vine, intra-vine and intra-bunch variability. The cluster analysis was applied to the plot with the most evident spatial structure (at Kilkis). Krigged maps of the SFRR, ANTHRG and yield were classified by k-means clustering in two-zones that differed significantly in their mean values. ANTHRG and SFRR were inversely correlated over 64% of the plot. SFRR appeared to be a potential useful proxy of yield since it was directly correlated to yield over 66% of the plot. The grape color (ANTHRG) was slightly higher over the low-yield zones with respect to the high-yield zones. Our study showed that the combination of anthocyanins and chlorophyll indices detected in the field on Crimson Seedless table grape by a portable fluorescence sensor can help in defining the best harvest time and the best areas for harvesting.<\/jats:p>","DOI":"10.3390\/s20040983","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"983","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Fluorescence-Sensor Mapping for the in Vineyard Non-Destructive Assessment of Crimson Seedless Table Grape Quality"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8298-7694","authenticated-orcid":false,"given":"Lorenza","family":"Tuccio","sequence":"first","affiliation":[{"name":"Istituto di Fisica Applicata \u201cNello Carrara\u201d IFAC, Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6279-8869","authenticated-orcid":false,"given":"Lucia","family":"Cavigli","sequence":"additional","affiliation":[{"name":"Istituto di Fisica Applicata \u201cNello Carrara\u201d IFAC, Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1199-8019","authenticated-orcid":false,"given":"Francesca","family":"Rossi","sequence":"additional","affiliation":[{"name":"Istituto di Fisica Applicata \u201cNello Carrara\u201d IFAC, Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9068-1660","authenticated-orcid":false,"given":"Olga","family":"Dichala","sequence":"additional","affiliation":[{"name":"American Farm School, 54 Marinou Antypa Street, P.O. Box 23, 55102 Thessaloniki, Greece"}]},{"given":"Fotis","family":"Katsogiannos","sequence":"additional","affiliation":[{"name":"American Farm School, 54 Marinou Antypa Street, P.O. Box 23, 55102 Thessaloniki, Greece"}]},{"given":"Ilias","family":"Kalfas","sequence":"additional","affiliation":[{"name":"American Farm School, 54 Marinou Antypa Street, P.O. Box 23, 55102 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0855-9389","authenticated-orcid":false,"given":"Giovanni","family":"Agati","sequence":"additional","affiliation":[{"name":"Istituto di Fisica Applicata \u201cNello Carrara\u201d IFAC, Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baseca, C.C., Sendra, S., Lloret, J., and Tomas, J. (2019). A Smart Decision System for Digital Farming. 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