{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:10:09Z","timestamp":1790691009205,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007620","name":"Department of Education of Liaoning Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007620","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012131","name":"Department of Science and Technology of Liaoning Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    Commercial greenhouses require temperature strategies that coordinate natural ventilation and screens with supplementary heating while maintaining a suitable crop environment. We evaluated a low input framework that combines a spectral geometry descriptor, one hour autoregressive prediction with exogenous inputs (ARX), temperature-conditioning degree-hour indices, vapor pressure deficit (VPD) exposure, and recorded vent, screen, and heating water data to screen temperature bands for field testing. The case study used 24,132 complete five minute interval starts from a 104\u00a0m \u00d7 68\u00a0m \u00d7 6.5\u00a0m Venlo tomato compartment operating at 1.25 plants\/m\n                    <jats:sup>2<\/jats:sup>\n                    , 3.75 productive stems\/m\n                    <jats:sup>2<\/jats:sup>\n                    , and an annual yield of approximately 45 kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    . The Rayleigh\u2013Ritz solution gave\n                    <jats:italic>\n                      C\n                      <jats:sub>G<\/jats:sub>\n                    <\/jats:italic>\n                    =\u00a07.188 m\n                    <jats:sup>2<\/jats:sup>\n                    , and the maximum single mode error was 4.26% across the numerical test grid. A preliminary fourth root scaling hypothesis mapped the declared reference half widths of 0.8\u20131.2 \u00b0C to candidate half widths of 0.97\u20131.45 \u00b0C; the 1.21 \u00b0C midpoint is an illustrative scenario conditional on the declared benchmark and reference half width. Alternative benchmarks produced multipliers of 1.047\u20131.290, confirming benchmark dependence. Within-site temporal holdout evaluation of ARX yielded RMSE 0.798 \u00b0C and\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.911; this result evaluates temporal prediction separately from geometry scaling. On the same observed temperature series, crop reference band membership increased from 21.02% at \u00b11.0 \u00b0C to 25.22% at \u00b11.21 \u00b0C, while the total degree-hour index decreased by 9.17%. The 9.17% value is a retrospective index difference rather than a measured energy saving. Only 28.38% of daytime records were within the 0.5\u20131.2 kPa VPD reference interval, showing that the prevailing moisture environment remained a constraint beyond temperature-band flexibility. The framework therefore supplies candidate bands and crop-environment criteria for controlled evaluation of energy use, plant responses, yield, and fruit quality.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1969599","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T13:36:28Z","timestamp":1790688988000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometry-informed temperature band screening for crop microclimate management in a commercial tomato greenhouse"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaojing","family":"Liu","sequence":"first","affiliation":[{"name":"College of Sciences, Shenyang Institute of Engineering","place":["Shenyang, China"]},{"name":"Osaka Central Advanced Mathematical Institute, Osaka Metropolitan University","place":["Osaka, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlin","family":"Li","sequence":"additional","affiliation":[{"name":"Horticulture Program, Dalian Art College","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongsheng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Sciences, Shenyang Institute of Engineering","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhui","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Sciences, Shenyang Institute of Engineering","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adapen.2022.100119","article-title":"Energy-efficient AI-based control of semi-closed greenhouses leveraging robust optimization in deep reinforcement learning","volume":"9","author":"Ajagekar","year":"2023","journal-title":"Adv. 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