{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:50:29Z","timestamp":1774079429220,"version":"3.50.1"},"reference-count":18,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"name":"EU","award":["UID\/50008\/2025"],"award-info":[{"award-number":["UID\/50008\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: \u2018Shrubs\u2019, \u2018Grasses\u2019, \u2018Legumes\u2019, and \u2018Others\u2019. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture.<\/jats:p>","DOI":"10.3390\/data11030063","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:50:36Z","timestamp":1773921036000},"page":"63","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pasture Plant\u2019s Dataset"],"prefix":"10.3390","volume":"11","author":[{"given":"Rafael","family":"Curado","sequence":"first","affiliation":[{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, Universidadede Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7696-4231","authenticated-orcid":false,"given":"Pedro","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, R. Cmte. Pinho e Freitas 28, 3750-127 \u00c1gueda, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5981-3829","authenticated-orcid":false,"given":"Maria R.","family":"Marques","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Investiga\u00e7\u00e3o Agr\u00e1ria e Veterin\u00e1ria, I.P. (INIAV), 2005-424 Vale de Santar\u00e9m, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6504-9441","authenticated-orcid":false,"given":"M\u00e1rio","family":"Antunes","sequence":"additional","affiliation":[{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, Universidadede Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ref_1","unstructured":"FAO (2025). The State of the World\u2019s Land and Water Resources for Food and Agriculture 2025, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Serrano, J., Roma, L., Shahidian, S., Belo, A.D.F., Carreira, E., Paniagua, L.L., Moral, F., Paix\u00e3o, L., and Marques da Silva, J. (2022). A Technological Approach to Support Extensive Livestock Management in the Portuguese Montado Ecosystem. Agronomy, 12.","DOI":"10.3390\/agronomy12051212"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Serrano, J., Matono, P., Carreira, E., Shahidian, S., Moral, F.J., Paniagua, L.L., Charneca, R., Pereira, A., and Belo, A. (2025). Pasture Floristic Composition as an Indicator of Soil PH Correction and Sheep Stocking Rate in Montado Ecosystem. Environments, 12.","DOI":"10.3390\/environments12100385"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tzanidakis, C., Tzamaloukas, O., Simitzis, P., and Panagakis, P. (2023). 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