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Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] By quantifying the apparent characteristic values of tobacco leaves at different time periods, the change law of tobacco water content was mastered, and the water content of tobacco leaves at different time periods was predicted according to the nonlinear characteristics of time series data of flue-cured tobacco water content and the strong correlation between flue-cured tobacco water content at adjacent periods. [Method] Image information technology was used to extract and analyze color of tobacco leaves [lightness value (L), red degrees (a), yellow (b), saturation degree value (C) and hue Angle (H) J and texture characteristic [ texture feature mean (m), standard deviation (er), smoothness (R), three moments (fL3), consistency (U) and texture entropy (e) J. Six apparent factors [lightness values (L) red degree value (a) yellow degree (b) texture means (m) standard (er) and entropy (e) texture J from principal component analysis were used as sample sets of network model. Kolmogorov theorem and test method were used to determine the prediction model for 13 hidden layer nodes. L-M (Levenberg-Marquardt) training algorithm used training fitting of limited time, to establish the topological structure of BP neural network model for the 6-13 -1 and predict water content of tobacco leaf. [Result] The changes of tobacco leaf image color features and texture features of the value were in accord with the tobacco leaf apparent reflect changes in baking process. This indicated that tobacco leaf image information conformed to the neural network input layer and output layer correlation model. The tobacco moisture content of the BP neural network prediction model (the determination coefficient R2 was 0. 9987 and RMSE root mean square error was 0. 0118) could well predict the moisture content of tobacco leaf in the different periods. [Conclusion] Therefore, the different periods of model of BP neural network model for predicting water in the tobacco leaf could be used to master dynamic change law of the moisture content of tobacco leaf. [ABSTRACT FROM AUTHOR]

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

【Objective】The present paper aimed to explore the research hotspots of mining wasteland reclamation and ecological restoration and analyze its trend.【Method】 Based on Web of Science core aggregate database as the data source, the research literature in the field of mining wasteland reclamation and ecological restoration from 2009 to 2019 was taken as the object of study. Based on the technology of text information visualization and Citespace, this paper introduced the latest research achievements and progress of mining wasteland reclamation and ecological restoration at home and abroad by establishing the relationship maps of cooperation characteristics and cooccurrence analysis through annual volume of papers, countries, institutions and authors. By using the methods of clustering cited documents and keyword co-occurrence analysis, this paper analyzed the research hotspots from the perspectives of scientific and technological text mining and bibliometrics, and summarized the development trend of this field in the past 10 years through the analysis of keyword co-occurrence. 【Result】 In recent years, the number of papers on mining wasteland increased year by year, more than doubling in 10 years. China’s research in the field of mining wasteland reclamation and ecological restoration was at a relatively advanced level. The contribution rate and centrality of the research papers were the first and third research fields, which were gradually becoming disciplinary, institutionalized and systematized, and tending to be perfect. Reclamation and land reclamation were the most frequent occurrences. The frequency of land, forest and climate change has increased significantly, which has gradually formed a new branch of research. 【Conclusion】 The research of mining wasteland reclamation and ecological restoration could be divided into two stages. From 2009 to 2015, the main focus was on the physical field, mainly from different land cover changes, accumulation density and plant population, etc. From 2016 to 2019, the main focus was on mining engineering, and related research was carried out on mining activities and the impact of mining wasteland on the surrounding areas. The research results can provide references for the development of mining wasteland reclamation and ecological restoration and the improvement of scientific research system. [ABSTRACT FROM AUTHOR]

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)