{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:44:53Z","timestamp":1776613493359,"version":"3.51.2"},"reference-count":14,"publisher":"Fuji Technology Press Ltd.","issue":"2","funder":[{"DOI":"10.13039\/501100007173","name":"Bio-oriented Technology Research Advancement Institution","doi-asserted-by":"publisher","award":["03020C1"],"award-info":[{"award-number":["03020C1"]}],"id":[{"id":"10.13039\/501100007173","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRM","J. Robot. Mechatron."],"published-print":{"date-parts":[[2026,4,20]]},"abstract":"<jats:p>\n                    This study presents a novel map-based energy consumption prediction model for agricultural electric vehicles operating in real orchard environments. Traditional static models often overlook resistance factors caused by varying terrain and soil conditions. To address this, we introduce an unknown resistance component\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>u<\/jats:italic>\n                    <\/jats:sub>\n                    , mapped spatially to reflect local environmental influences such as slope and soil hardness. Field experiments were conducted in a vineyard in Hokkaido, Japan, using GNSS and battery data collected at 10 Hz during uphill and downhill runs. The proposed model achieved a maximum mean absolute percentage error of 2.3%, significantly outperforming conventional models. A notable negative correlation between\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>u<\/jats:italic>\n                    <\/jats:sub>\n                    and soil hardness was observed, confirming that softer soils increase vehicle resistance. Simulations of continuous operations across adjacent routes further demonstrated reduced cumulative prediction errors, supporting applications in route planning and battery management.\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>u<\/jats:italic>\n                    <\/jats:sub>\n                    <jats:italic>(x,y)<\/jats:italic>\n                    is currently treated as static, and future work will expand it to a spatiotemporal parameter\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>u<\/jats:italic>\n                    <\/jats:sub>\n                    <jats:italic>(x,y,t)<\/jats:italic>\n                    to incorporate dynamic environmental changes. Online learning and validation across diverse terrains are also planned. This approach enhances model adaptability, offering a reliable tool for energy-efficient and sustainable operation of electric vehicles in agriculture.\n                  <\/jats:p>","DOI":"10.20965\/jrm.2026.p0388","type":"journal-article","created":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:02:06Z","timestamp":1776610926000},"page":"388-397","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Map-Based Energy Consumption Model for Orchard Electric Vehicles Using Field Data"],"prefix":"10.20965","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6898-4148","authenticated-orcid":true,"given":"Tomoaki","family":"Hizatate","sequence":"first","affiliation":[{"name":"Graduate School of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-8589, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4433-6193","authenticated-orcid":true,"given":"Noboru","family":"Noguchi","sequence":"additional","affiliation":[{"name":"Research Faculty of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-8589, Japan"}]}],"member":"8550","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"key-10.20965\/jrm.2026.p0388-1","doi-asserted-by":"crossref","unstructured":"N. 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Bothtis, \u201cAgricultural operations planning in fields with multiple obstacle areas,\u201d Comput. Electron. Agric., Vol.109, pp. 12-22, 2014. https:\/\/doi.org\/10.1016\/J.COMPAG.2014.08.013","DOI":"10.1016\/j.compag.2014.08.013"},{"key":"key-10.20965\/jrm.2026.p0388-5","doi-asserted-by":"crossref","unstructured":"D. D. Bochtis and S. G. Vougioukas, \u201cMinimising the non-working distance travelled by machines operating in a headland field pattern,\u201d Biosyst. Eng., Vol.101, No.1, pp. 1-12, 2008. https:\/\/doi.org\/10.1016\/J.BIOSYSTEMSENG.2008.06.008","DOI":"10.1016\/j.biosystemseng.2008.06.008"},{"key":"key-10.20965\/jrm.2026.p0388-6","doi-asserted-by":"crossref","unstructured":"A. Utamima and A. Djunaidy, \u201cAgricultural routing planning: A narrative review of literature,\u201d Procedia Comput. 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