# Detection of Hidden Pest Rice Weevil (Sitophilus oryzae) in Wheat Kernels Using Hyperspectral Imaging

## TL;DR

This study uses hyperspectral imaging to detect hidden rice weevils in wheat kernels, offering a nondestructive and accurate method for early pest detection.

## Contribution

The study introduces a nondestructive method using hyperspectral imaging and advanced modeling to detect rice weevil infestation in wheat.

## Key findings

- MSC preprocessing improved classification performance by reducing spectral noise.
- The MSC-CARS-SVM model achieved high accuracy for early and late infestation stages.
- Hyperspectral imaging proved feasible for nondestructive detection of rice weevil in wheat.

## Abstract

The rice weevil (Sitophilus oryzae) is a major pest in stored wheat, and traditional detection methods face challenges in identifying its hidden life stages within kernels. This study develops a nondestructive method to detect S. oryzae (Sitophilus oryzae) infestation in wheat kernels using hyperspectral imaging, spectral preprocessing, feature extraction, and classification modeling. Hyperspectral data were collected from wheat kernels at different infestation stages (1, 11, 21, and 25 days (d)) and from healthy kernels. Spectral quality was optimized using SG smoothing, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Feature extraction algorithms, including Competitive Adaptive Re-weighting Algorithm (CARS), Successive Projection Algorithm (SPA), and Iterative Retention of Information Variables (IRIV), were used to reduce data dimensionality, while classification models like Decision Tree (DT), K-nearest neighbors (KNN), and Support Vector Machine (SVM) were applied. The results show that MSC preprocessing provides the best performance among the models. After feature band selection, the MSC-CARS-SVM model achieved the highest accuracy for the 1 day and 25 d samples (95.48% and 96.61%, respectively). For the 11 d and 21 d samples, the MSC-IRIV-SPA-SVM model achieved the best performance with accuracies of 94.35% and 94.92%, respectively. This study demonstrates that MSC effectively reduces spectral noise and improves classification performance. After feature selection, the model shows significant improvements in both accuracy and stability. The study confirms the feasibility of using hyperspectral technology to identify healthy and S. oryzae-infested wheat kernels, providing theoretical support for early, nondestructive pest detection.