{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:17:01Z","timestamp":1775251021859,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T00:00:00Z","timestamp":1539561600000},"content-version":"vor","delay-in-days":287,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31471412"],"award-info":[{"award-number":["31471412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants\u2019 electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.<\/jats:p>","DOI":"10.1155\/2018\/9250832","type":"journal-article","created":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T19:31:36Z","timestamp":1539631896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Feature Recognition of Crop Growth Information in Precision Farming"],"prefix":"10.1155","volume":"2018","author":[{"given":"Hanqing","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-2448","authenticated-orcid":false,"given":"Xiaohui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhou","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Xi","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,10,15]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jplph.2008.06.004"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnsc.2008.08.009"},{"key":"e_1_2_10_3_2","first-page":"192","article-title":"A vista on growth information transmissions of weak electrical signals in plants","volume":"21","author":"Wang L.-Z.","year":"2010","journal-title":"China Jiliang University News"},{"key":"e_1_2_10_4_2","first-page":"10","article-title":"Research progress of plant electrical signal test","volume":"19","author":"Wang L.-z.","year":"2008","journal-title":"China Jiliang University News"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0270(00)00356-3"},{"key":"e_1_2_10_6_2","first-page":"209","article-title":"Analysis of Characteristics of Electrical Signals in the Leaves of Three Species of Plants","volume":"27","author":"Xiao-li M.","year":"2011","journal-title":"Journal of Xi\u2032an University of Technology"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11434-011-4820-5"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/10.709563"},{"key":"e_1_2_10_9_2","first-page":"116","article-title":"Application of wavelet entropy in denoising processing and R wave detection of ECG signal","volume":"46","author":"Hong-hua H.","year":"2010","journal-title":"Computer Engineering and Applications"},{"key":"e_1_2_10_10_2","first-page":"238","article-title":"Wavelet packet transform and principal component analysis for identifying sub-health state","volume":"47","author":"Ai-hua Z.","year":"2011","journal-title":"Computer Engineering and Applications"},{"key":"e_1_2_10_11_2","volume-title":"Neural network theory and MATLAB2007 implementation","author":"Zhe-xue G."},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jplph.2013.01.009"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jplph.2010.06.003"},{"key":"e_1_2_10_14_2","doi-asserted-by":"crossref","unstructured":"WangL.andLiQ. 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