{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:00:11Z","timestamp":1753887611330,"version":"3.41.2"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":327,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2020ZDZX1060"],"award-info":[{"award-number":["2020ZDZX1060"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010843","name":"Guangzhou Science, Technology and Innovation Commission","doi-asserted-by":"publisher","award":["201803020033","201704020030"],"award-info":[{"award-number":["201803020033","201704020030"]}],"id":[{"id":"10.13039\/501100010843","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004000","name":"Guangzhou Municipal Science and Technology Program key projects","doi-asserted-by":"publisher","award":["201903010063","202002030154"],"award-info":[{"award-number":["201903010063","202002030154"]}],"id":[{"id":"10.13039\/501100004000","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in <jats:italic>Agastache rugosa<\/jats:italic> cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder\u2010hierarchical extreme learning machine\u2010softmax (SSDA\u2010HELM\u2010SOFTMAX) was put forward to achieve accurate and real\u2010time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA\u2010HELM\u2010SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN\u2010SOFTMAX, and SAE\u2010SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.<\/jats:p>","DOI":"10.1155\/2021\/1015391","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T21:50:17Z","timestamp":1637790617000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA\u2010HELM\u2010SOFTMAX Model"],"prefix":"10.1155","volume":"2021","author":[{"given":"Juan","family":"Zou","sequence":"first","affiliation":[]},{"given":"Hanjing","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Qingxiu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ningxia","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-5057","authenticated-orcid":false,"given":"Ting","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5182-3937","authenticated-orcid":false,"given":"Ling","family":"Yang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024522111341"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2020.04.002"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2947125"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/1521-4109(200202)14:4<273::AID-ELAN273>3.0.CO;2-5"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/elan.1140041006"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/09540109809354998"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0021-9673(97)00507-4"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2014.11.105"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2016.02.090"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.5120\/15193-3573"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.9b05110"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1021\/ac403257p"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_2_11_14_2","article-title":"Hybrid population particle algorithm and multi-quantile robust extreme learning machine based short-term wind speed forecasting","volume":"47","author":"Lu D.","year":"2019","journal-title":"Power System Protection and Control"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-018-9869-6"},{"key":"e_1_2_11_16_2","first-page":"49","article-title":"Fault diagnosis of transformer based on extreme learning machine optimized by genetic algorithm","volume":"51","author":"Zhong L.","year":"2015","journal-title":"High Voltage Apparatus"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2424995"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6025381"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2938890"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2951708"},{"key":"e_1_2_11_22_2","first-page":"49","article-title":"Fault diagnosis of rolling bearing based on dual-treecomplex wavelet packet transform","volume":"29","author":"Xu Y.","year":"2013","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"key":"e_1_2_11_23_2","article-title":"Comparison of wavelet denoise and FFT denoise","volume":"3","author":"Hao W.","year":"2011","journal-title":"Electric Power Science and Engineering"},{"key":"e_1_2_11_24_2","first-page":"1140","article-title":"Fast leave-one-out cross-validation algorithm for extreme learning machine","volume":"45","author":"Liu X. Y.","year":"2011","journal-title":"Journal of Shanghai Jiaotong University"},{"key":"e_1_2_11_25_2","article-title":"MRI brain image recognition method based on improved L-BFGS sparse denoising autoencoder","volume":"40","author":"Wang X.-y.","year":"2019","journal-title":"Journal of Graphics"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.001.0001"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/1015391.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/1015391.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/1015391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:46:20Z","timestamp":1722905180000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/1015391"}},"subtitle":[],"editor":[{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/1015391"],"URL":"https:\/\/doi.org\/10.1155\/2021\/1015391","archive":["Portico"],"relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"type":"print","value":"1687-725X"},{"type":"electronic","value":"1687-7268"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1015391"}}