Deep learning has become a powerful tool for various image-based applications, including the detection of agricultural pests. However, the deployment of deep neural networks in resource-constrained agricultural settings poses significant challenges due to their high computational demands. Traditional pruning methods designed to mitigate this challenge often result in sparse networks that may not perform efficiently on hardware optimized for dense operations. In this study, we present a novel approach called Efficient Sparse Tensor Core Networks (STCN), which leverages the inherent sparsity of neural networks to enhance computational efficiency significantly. Our method integrates a specialized sparse network architecture with hardware-accelerated tensor operations, optimized for real-time analysis on edge devices. We have tailored our model to exploit sparse tensor operations, facilitating faster computations and reduced energy consumption without compromising accuracy. When applied to the IP102 dataset, a comprehensive collection of insect pest images, the STCN method proves effective in classifying various pest species under different environmental conditions. Initial experiments show that our approach not only maintains a high classification accuracy of 92% but also achieves a substantial reduction in computational workload, reducing energy consumption by 30% and improving processing speed by 40% during inference. These findings highlight the potential of STCN for real-time pest detection, paving the way for the practical application of advanced AI in precision agriculture, optimizing resource utilization, and promoting sustainable farming practices.