This study presents an advanced automatic thrips counting and classification system, leveraging a novel SuperResolution (SR) technique, named KSVD_DR to enhance image analysis accuracy. We developed and validated detection models using a diverse dataset that included high-resolution scanned images and smartphone-captured images of blue and yellow traps, both with and without plastic wrap. This approach ensured robust performance across various real-world agricultural settings. The application of SR improved the detection accuracy from $66.5 \%$ to $\mathbf{8 9. 7} \%$, as measured by the mean Average Precision at $\mathbf{5 0 \%}$ Intersection over Union (mAP50). The overall testing accuracy achieved was $81.2 \%$, with specific accuracies of $80.3 \%$ for images with plastic wrap and $83.4 \%$ for those without confirming the system’s effectiveness in both laboratory and field conditions. Additionally, SR processing enhanced thrips classification accuracy from $58.5 \%$ to $\mathbf{6 5. 3 \%}$ across six distinct thrips classes, demonstrating its potential to refine species-specific identification. Future developments will focus on expanding outdoor data collection to validate and enhance system performance under varying environmental conditions and to improve detection accuracy at higher confidence levels. The study also aims to refine the classification model by incorporating more diverse data inputs and exploring advanced machine learning techniques, enhancing the ability to differentiate between thrips species effectively.