Pests and insects pose a significant threat to global agriculture, causing crop damage and quality loss, with a massive annual costs. Early detection of these agricultural challenges is essential for sustainable farming and food security. Challenges in early pest detection include class imbalance, computational limitations, and the need for comprehensive feature learning. In response, our study introduces “Tri Focus Net (TFN),” a hybrid model that seamlessly integrates multiple attention mechanisms, including Channel, Soft, and Squeeze-and-Excitation Attentions, effectively addressing these issues. This combination empowers TFN to discern critical details both globally and locally within images, significantly enhancing feature extraction and robustness. Additionally, we have successfully integrated DenseNet 201 with TFN, further enhancing our model’s feature extraction capabilities and robustness. In our work, we achieved exceptional results, attaining a maximum accuracy of 94.20% for the insects dataset and a perfect 100% accuracy for the pest dataset. Our contribution to agriculture lies in providing a cost-effective, precise, and robust solution for pest and insect detection, underpinning the sustainability of crop health and the agricultural industry.