<![CDATA[Agricultural pest surveillance in open field environments presents a persistent challenge for food security, particularly when early detection is limited by the inadequacy of traditional monitoring methods. Existing Spectral Residual (SR) algorithms, while capable of saliency-based object detection, exhibit notable limitations in adapting to dynamic agricultural conditions resulting in degraded segmentation quality and elevated false-positive rates. This study proposes an improved Spectral Residual algorithm that independently processes Red, Green, and Blue (RGB) channels via Fast Fourier Transform (FFT), preserving richer spectral information that is otherwise lost in conventional grayscale-based approaches. The proposed pipeline integrates spatial saliency maps with temporal motion cues extracted through frame-difference analysis, suppressing static background noise while reinforcing responses in regions exhibiting genuine movement. Adaptive binarization and Support Vector Machine (SVM) classification are further incorporated to strengthen object segmentation robustness across diverse lighting and background conditions. The model was evaluated on 136 frames extracted from 2MP Hikvision CCTV footage installed at rice field sites, benchmarked against the classical Spectral Residual baseline using accuracy, precision, recall, and F1-score. The proposed method achieved an accuracy of 98.15%, precision of 91.67%, recall of 98.98%, and F1-score of 95.18% — representing substantial improvements over the baseline (74.00%, 70.00%, 65.00%, and 69.00%, respectively). These results demonstrate that multi-channel spectral processing combined with motion-aware saliency yields a robust and computationally efficient saliency framework whose low algorithmic complexity makes it a strong candidate for real-time sparrow pest detection in CCTV-based agricultural monitoring systems.]]>
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