Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on...

Area of Science:

• Forestry Science
• Agricultural Technology
• Digital Image Processing

Background:

• Sandalwood cultivation requires efficient nutritional diagnosis.
• Existing methods for assessing tree nutrient content can be time-consuming and labor-intensive.
• Real-time, automated systems are needed for practical application in forestry.

Purpose of the Study:

• To develop a real-time, automated system for predicting total nitrogen content in sandalwood.
• To introduce a novel segmentation algorithm and a "growth status" (GS) indicator for nutritional analysis.
• To evaluate the effectiveness of the error-in-variable model for parameter estimation in this context.

Main Methods:

• Digital image processing of sandalwood using field-acquired images.
• Development of a new segmentation algorithm incorporating Lab color channels, Otsu method, median filtering, and morphological operations.
• Definition and application of the "growth status" (GS) indicator, including GSMER and GSMCC metrics.
• Utilizing an error-in-variable model for parameter estimation, comparing it with the least squares method.

Main Results:

• The combination of Lab color channels with image processing techniques effectively separated sandalwood from the background.
• The "growth status" (GS) indicator, particularly GSMCC, improved model fitting for nitrogen content prediction.
• The error-in-variable model demonstrated enhanced accuracy and precision compared to the least squares method.
• An optimal model was identified for predicting total nitrogen content: [Formula: see text].

Conclusions:

• Digital image processing, combined with a novel GS indicator, provides an effective method for sandalwood nutritional diagnosis.
• The error-in-variable model offers improved accuracy for predicting nitrogen content in sandalwood.
• This research highlights the potential of Internet of Things (IoT) technology in precision forestry and sustainable sandalwood management.