{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T15:13:47Z","timestamp":1789139627322,"version":"build-2803163510"},"reference-count":90,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100022889","name":"Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100022889","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    The tree tomato (\n                    <jats:italic>Solanum betaceum Cav<\/jats:italic>\n                    .) is a Solanaceae fruit native to South America with nutritional, functional, and economic value. However, assessing fruit maturity and making harvest-related decisions continue to rely primarily on manual visual inspection, which can be subjective and inconsistent under field conditions. The objective of this study was to evaluate the performance of YOLO11-based object detection models for the automatic detection of the ripeness of\n                    <jats:italic>Solanum betaceum<\/jats:italic>\n                    fruits in a real-world agricultural setting. To this end, a dataset of 200 images captured with smartphones was used, in which 1,361 fruits were annotated with bounding boxes corresponding to green and ripe fruits. The YOLO11n, YOLO11s, and YOLO11m models were trained with an input resolution of 640. For each setting, cross-validation (k = 5) was used, and data augmentation was appliedss-validation (k=5) for each setting and applying data augmentation to the training subsets. YOLO11m achieved the best overall performance, as it combined high values fr precision, recall, and mAP. On the test set, YOLO11m achieved an precision of 0.9878 \u00b1 0.0055, a recall of 0.9750 \u00b1 0.148, and an mAP50 of 0.9618 \u00b1 0.0188 for green fruit, as well as a precision of 0.9950 \u00b1 0.0041, a recall of 0.9958 \u00b1 0.0039, and a mAP50 of 0.9920 \u00b1 0.0047 for ripe fruits. YOLO11s also demonstrated competitive performance, particularly in the detection of ripe fruits, with an mAP50 of 0.9894 \u00b1 0.0070 and an mAP50\u201395 of 0.9754 \u00b1 0.0081, positioning it as an efficient alternative given its substantially lower computational cost. In contrast, YOLO11n, the lightest architecture, showed the lowest performance for green fruits (mAP50-95: 0.8557 \u00b1 0.0611), reflecting the limitations of its 7representational capacity.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1880899","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T14:54:42Z","timestamp":1789138482000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of Solanum betaceum Cav fruit maturity using YOLO11-based deep learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Lenin","family":"Qui\u00f1ones Huatangari","sequence":"first","affiliation":[{"name":"Escuela Profesional de Ingenier\u00eda de Ciencia de Datos e Inteligencia Artificial, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e9ctor Vladimir","family":"V\u00e1squez P\u00e9rez","sequence":"additional","affiliation":[{"name":"Laboratorio de Agrostolog\u00eda, Instituto de Investigaci\u00f3n en Ganader\u00eda y Biotecnolog\u00eda, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lamberto","family":"Valqui-Valqui","sequence":"additional","affiliation":[{"name":"Laboratorio de Agrostolog\u00eda, Instituto de Investigaci\u00f3n en Ganader\u00eda y Biotecnolog\u00eda, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Manuel","family":"Palomino Ojeda","sequence":"additional","affiliation":[{"name":"Data Science Research Institute, Universidad Nacional de Ja\u00e9n","place":["Ja\u00e9n, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Saravia","sequence":"additional","affiliation":[{"name":"Estaci\u00f3n Experimental Agraria Amazonas, Instituto Nacional de Innovacion Agraria","place":["La Molina, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Bardales Escalante","sequence":"additional","affiliation":[{"name":"Laboratorio de Agrostolog\u00eda, Instituto de Investigaci\u00f3n en Ganader\u00eda y Biotecnolog\u00eda, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gelver","family":"Silva","sequence":"additional","affiliation":[{"name":"Estaci\u00f3n Experimental Agraria Amazonas, Instituto Nacional de Innovacion Agraria","place":["La Molina, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antony","family":"Chavez-Jalk","sequence":"additional","affiliation":[{"name":"Estaci\u00f3n Experimental Agraria Amazonas, Instituto Nacional de Innovacion Agraria","place":["La Molina, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omer","family":"Cruz Caro","sequence":"additional","affiliation":[{"name":"Oficina de Gestion de la Calidad, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leandro","family":"Valqui","sequence":"additional","affiliation":[{"name":"Laboratorio de Agrostolog\u00eda, Instituto de Investigaci\u00f3n en Ganader\u00eda y Biotecnolog\u00eda, Universidad Nacional Toribio Rodr\u00edguez de Mendoza de Amazonas","place":["Chachapoyas, Peru"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.5281\/ZENODO.7900479","article-title":"Fruit ripeness assertion using deep learning","volume":"2","author":"Ajil","year":"2023","journal-title":"Int. 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