{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T07:09:02Z","timestamp":1770707342878,"version":"3.49.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>High-throughput plant phenotyping (HTPP) enhances the throughput, resolution, and dimensionality of conventional manual phenotyping techniques. However, existing platforms face significant challenges, including high acquisition and maintenance costs, limited adaptability to field conditions, and inadequate data management capabilities. This paper introduces GREENTRIBE, an open-source, multi-sensor HTPP architecture that integrates Internet of Things sensing devices and robotics to collect, process, and manage comprehensive phenotypic and environmental data. GREENTRIBE features a multiscale sensing network, built on a sensor-independent communication protocol. An ontology-driven data management layer was designed in accordance with common standards and metadata guidelines, ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) (meta)data. The architecture combines Computer Vision and Artificial Intelligence data analysis pipelines with a process-based crop model for data assimilation, allowing the quantitative traits derived from the sensing layer to be linked to contextual data (genotype, environment, and management conditions). The architecture and performance indicators are presented, demonstrating efficient data collection, processing, and management. Phenotyping is the cornerstone of GREENTRIBE, offering a valuable platform for generating data-rich, reproducible workflows, multimodal datasets, and analysis systems with high impact on Precision Agriculture, improving real-time monitoring, input application, and environmental impacts assessment towards maximized crop productivity, quality, and sustainability.<\/jats:p>","DOI":"10.2139\/ssrn.6203121","type":"posted-content","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:41:46Z","timestamp":1770644506000},"source":"Crossref","is-referenced-by-count":0,"title":["GREENTRIBE: An Open-Source Multi-Sensor High-Throughput Plant Phenotyping Framework for Indoor Facilities"],"prefix":"10.2139","author":[{"given":"Leandro","family":"Rodrigues","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2807-2506","authenticated-orcid":true,"given":"Francisco","family":"Terra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7948-0104","authenticated-orcid":true,"given":"Pedro","family":"Rodrigues","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3898-9841","authenticated-orcid":true,"given":"Pedro","family":"Moura","sequence":"additional","affiliation":[]},{"given":"Filipe Neves dos","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-324X","authenticated-orcid":true,"given":"M\u00e1rio","family":"Cunha","sequence":"additional","affiliation":[]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:41:46Z","timestamp":1770644506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=6203121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.6203121","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}