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This paper presents an enhanced YOLOX-based algorithm for pest detection, adopting a nature-inspired approach for refining its methodology. To tackle the limited availability of image data pertaining to pests and diseases, the paper incorporates Mosaic and Mixup technologies for effective image preprocessing. Furthermore, a novel training strategy is proposed to enhance the overall quality of the results. The existing architecture is enriched by integrating shallow information, while the CLT module is devised to facilitate cross-layer fusion and extract essential feature information. This advancement enables improved object detection across various scales. Additionally, the paper optimizes the original PFPN structure by eliminating the convolutional layer preceding upsampling, enhancing the C3 module, and integrating the convolutional attention model (CBAM) to identify salient regions within complex scenes. The performance of the proposed CLT-YOLOX model is extensively evaluated using the IP102 dataset, demonstrating its effectiveness. Notably, the model exhibits significant improvements compared to the original AP evaluation index, with an increase of 2.2% in average precision (mAP) and 1.8% in AP₇₅. Furthermore, favorable results are achieved in the COCOmAP index, particularly in the AP_small category where there is a 2.2% improvement in performance.