{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T01:05:15Z","timestamp":1783731915683,"version":"3.55.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.<\/jats:p>","DOI":"10.3233\/jifs-211507","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T19:05:14Z","timestamp":1633633514000},"page":"2181-2191","source":"Crossref","is-referenced-by-count":3,"title":["A novel two-stage method of plant seedlings classification based on deep learning"],"prefix":"10.1177","volume":"42","author":[{"given":"Tianhong","family":"Dai","sequence":"first","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Cong","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Huang","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwang","family":"Huang","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiancheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxue","family":"Sun","sequence":"additional","affiliation":[{"name":"Electromechanic Engineering College, Northeast Forestry University, Harbin, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211507_ref1","first-page":"34","article-title":"Research on Plant Leaf Classification Method Based on PCA and SVM","author":"Zhang","year":"2013","journal-title":"Agricultural Mechanization Research"},{"key":"10.3233\/JIFS-211507_ref2","first-page":"61","article-title":"Image recognition method of plant leaves based on WLLE and SVM","author":"Ding","year":"2013","journal-title":"Journal of Anhui University (Self Science Edition)"},{"key":"10.3233\/JIFS-211507_ref3","first-page":"151","article-title":"Plant species classification using deep convolutional neural network","author":"Dyrmann","year":"2016","journal-title":"Biosystems Engineering"},{"key":"10.3233\/JIFS-211507_ref4","unstructured":"Giselsson T.M. , J\u00f8rgensen R. , JensenP.K., DyrmannM. and MidtibyH.S., A Public Image Database for Benchmark of Plant Seedling Classification Algorithms, 2017."},{"key":"10.3233\/JIFS-211507_ref5","first-page":"154","article-title":"AgroAVNET for crops and weeds classification: A step forward in automatic farming","author":"Chavan","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-211507_ref6","first-page":"10","article-title":"Convolutional Neural Network Architecture for Plant Seedling Classification","author":"Elnemr","year":"2019","journal-title":"International Journal of Advanced Computer Science and Applications (IJACSA)"},{"key":"10.3233\/JIFS-211507_ref7","unstructured":"Nkemelu D. , Omeiza D. and Lubalo N. , Deep Convolutional Neural Network for Plant Seedlings Classification. 2018."},{"key":"10.3233\/JIFS-211507_ref8","first-page":"7","article-title":"Deep learning-based Automatic Weed Detection on Onion Field","author":"Jeong","year":"2018","journal-title":"Smart Media Journal"},{"key":"10.3233\/JIFS-211507_ref9","first-page":"51","article-title":"Low-Altitude Remote Sensing Based on Convolutional Neural Network for Weed Classification in Ecological Irrigation Area","author":"Wang","year":"2018","journal-title":"IFAC PapersOnLine"},{"key":"10.3233\/JIFS-211507_ref10","first-page":"7","article-title":"Plant Seedlings Classification Using Deep Learning","author":"Ashqar","year":"2019","journal-title":"International Journal of Academic Information Systems Research"},{"key":"10.3233\/JIFS-211507_ref11","doi-asserted-by":"crossref","unstructured":"Alimboyong C.R. and Hernandez A.A. , An Improved Deep Neural Network for Classification of Plant Seedling Images. 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