{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:27:46Z","timestamp":1774121266889,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T00:00:00Z","timestamp":1550448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.<\/jats:p>","DOI":"10.3390\/sym11020256","type":"journal-article","created":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T04:08:20Z","timestamp":1550549300000},"page":"256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5121-7192","authenticated-orcid":false,"given":"Jiangyong","family":"An","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-7132","authenticated-orcid":false,"given":"Wanyi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Maosong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Sanrong","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Huanran","family":"Yue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.fcr.2016.10.018","article-title":"Trends in drought tolerance in Chinese maize cultivars from the 1950s to the 2000s","volume":"201","author":"Sun","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1080\/00103624.2016.1228953","article-title":"Influence of Drought Applied at Different Growth Stages on Kernel Yield and Quality in Maize (Zea Mays L.)","volume":"47","author":"Anwar","year":"2016","journal-title":"Commun. 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