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Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 8, p e0202649 (2018)
- Publication Year :
- 2018
- Publisher :
- Public Library of Science, 2018.
-
Abstract
- This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named “growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GSMCC performs better than GSMER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is y=237.374e−(4.471LL′+11.927aa′+2.782bb′)+26.248GSMCC−4.274. This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species.
- Subjects :
- 0106 biological sciences
Chlorophyll
Pigments
Leaves
Chloroplasts
Image Processing
lcsh:Medicine
Plant Science
01 natural sciences
Trees
Mathematical and Statistical Techniques
Digital image processing
Image Processing, Computer-Assisted
Segmentation
lcsh:Science
Mathematics
Multidisciplinary
biology
Plant Anatomy
Eukaryota
Agriculture
Forestry
04 agricultural and veterinary sciences
Plants
Physical Sciences
symbols
Engineering and Technology
Cellular Structures and Organelles
Cellular Types
Agrochemicals
Algorithms
Research Article
Accuracy and precision
Imaging Techniques
Nitrogen
Plant Cell Biology
Materials Science
Image processing
Research and Analysis Methods
Otsu's method
symbols.namesake
Plant Cells
Median filter
Fertilizers
Materials by Attribute
Sandalwood
Pixel
Organic Pigments
business.industry
lcsh:R
Organisms
Biology and Life Sciences
Pattern recognition
Cell Biology
biology.organism_classification
Plant Leaves
Santalum
Signal Processing
Exponential Functions
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
lcsh:Q
Artificial intelligence
business
Mathematical Functions
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....ef1b986f869e8b5a28ca4f809d1116fd