Due to the difficulty of researching insects, we do not know what the future holds for insect population patterns, but we do know that insect numbers and species variety are dropping. Manually capturing and identifying species takes time. The significant advances in deep learning (DL) technology have helped several fields. Autonomous insect pest monitoring is one. Deep learning introduces many new features, but it also makes insect detection categorization tougher. In this study, we used a deep-learning hybrid classification model to identify insects in outdoor pictures. To enhance DCNN classification and extract high-level information from photos, the Regular VGG model was replaced with the FVGG16 (FineTuned-VGG16) model. To accomplish the aims, this was done. A fine-tuned deep convolutional neural network (DCNN) model of a VGG16 underpins the suggested approach. We present a model that can recognize insects in their natural surroundings and classify them by their properties. This model classifies agricultural insects with precision of 98.71°, sensitivity of 97.85°, F-score of 98.28°, specificity of 89.44°, and accuracy of 96.94°.