Wood Type Identification via Neural Networks and Spectral Analysis: An Advanced Algorithmic Solution

Forestry management, woodworking, and manufacturing need wood type identification. This study introduces a neural network-spectral analysis technique for accurate and automatic wood type detection. Principal Component Analysis (PCA) is used to extract features from a heterogeneous collection of wood spectral signatures after training a neural network. The algorithm’s 94.2% accuracy on a testing dataset shows its ability to distinguish wood kinds.The model’s confusion matrix shows it can recognise closely related wood species with few misclassifications. The neural network’s precision, recall, and F1 score prove its wood classification accuracy. With PCA highlighting classification characteristics, spectral analysis helps the algorithm succeed.The method is useful for forestry management and woodworking quality control. The non-destructive technology provides in-situ wood type detection, addressing environmental and conservation issues. The study explores ramifications, constraints, and future algorithm modification and application in real-world contexts.Neural networks and spectral analysis provide a strong, efficient, and non-destructive wood type detection solution. The hopeful results represent a major advance in wood science and current computer methods, with applicability across sectors.