{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:52:35Z","timestamp":1769842355411,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T00:00:00Z","timestamp":1629590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673281"],"award-info":[{"award-number":["61673281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["61903264"],"award-info":[{"award-number":["61903264"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.<\/jats:p>","DOI":"10.3390\/info12080336","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T21:47:52Z","timestamp":1629668872000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms"],"prefix":"10.3390","volume":"12","author":[{"given":"Yonggang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5188-6313","authenticated-orcid":false,"given":"Ruimin","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhi","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,22]]},"reference":[{"key":"ref_1","first-page":"25","article-title":"Focus on lycopene","volume":"797","author":"Leoni","year":"2003","journal-title":"Acta Hort."},{"key":"ref_2","unstructured":"FAO (2016, February 10). 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