Crown profile models were developed for Chinese fir (Cunninghamia lanceolata) in Shunchang County, Fujian Province. We used data from 360 trees located in 65 pure, even-aged, and unthinned temporary plots. The data was divided into three groups according to site index. Nine models, including models for upper crown, lower crown, and entire crown, were fit; the optimal ones in each group were validated and chosen to estimate crown shape. The optimal models explained at least 70% variability in crown radius. In addition, models for crown width, height above ground to crown base, and height above ground to largest crown radius were also developed to facilitate the convenient simulation of crown profile models. These three models explained 85.4%, 85.1%, and 86.9%variability. All models also passed the F-test and residual test. The 3D images of a single tree and stands were presented by OpenGL technology on visual c++ platform based on the proposed models. Tree growth was compared and analyzed using crown profile curves under constrained conditions. The analysis results accorded with plant growth.
In china, there is a great variety of sheep production systems, from the most traditional grazing types, mixed (grazing/stabling) to the most technologically advanced facility sheep. Body size parameters can reflect its growth development, production performance and genetic characteristics. So, monitoring body size is very important. In view of the problems or limitations of the present manual measurement, using the tools of measuring stick, tape measure, etc., the sheep having to stand on a flat place with correct posture, a non-contact method for measuring body dimensions of small-tailed Han sheep based on machine vision has been proposed and discussed. This approach is based on a position limit apparatus, computer-assisted visual capture, an automatic foreground extraction algorithm and a measuring point detection algorithm. The measured body sizes include withers height, back height, rump height, body length, chest depth, chest width, abdominal width and rump width. This approach has been examined in a specific farm for a case study. The errors of more than 90% measurements for sheep body size are within 3%. The results indicate that the method based on visual image analysis is effective, and it is especially suitable for the sheep feeding in an intensive and largescale way.ARTICLE HISTORY
Judging and predicting tree suitability is of great significance in the cultivation and management of forests. Background and Objectives: Due to the diversity of tree species for afforestation in China and the lack of experts or the limitations of expert knowledge, the site rules of tree species in some regions are lacking or incomplete, so that a small number of tree suitability empirical site rules are difficult to adapt to the afforestation expert system’s diverse needs. Research Highlights: This paper explores an intelligent method to automatically extract rules for selecting favorable site conditions (tree suitability site rules) from a large amount of data to solve the problem of knowledge acquisition, updating and maintenance of suitable forest site rules in the expert system. Materials and Methods: Based on the method of site quality evaluation and the theory of the decision tree in knowledge discovery and machine learning, the dominant species of Chinese fir and Masson pine in the forest resources subcompartment data (FRSD) of Jinping County, Guizhou Province were taken as examples to select the important site factors affecting the forest quality and based on the site quality of potential productivity. Assessment methodology was proposed to determine the afforestation of a stand site by nonlinear quantile regression, the decision tree was constructed from the ID3, C5.0 and CART algorithms. Results: Finally, the best-performing CART algorithm was selected to construct the model, and the extractor of the afforestation rules was constructed. After validating the rules for selecting favorable site conditions of Chinese fir and Masson pine, the production representation method was used to construct the relationship model of the knowledge base. Conclusions: Intelligent extraction of suitable tree rules for afforestation design in an expert system was realized, which provided the theoretical basis and technical support for afforestation land planning and design.
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