The objective of the work is to create and deploy a sophisticated pest detection system in agricultural areas that achieves an amazing 99.7% accuracy rate by utilizing the EfficientNet-B4 model. Crop productivity and food security are seriously threatened by pests, and conventional pest management techniques are frequently inaccurate and ineffective. The research makes use of cutting- edge deep learning methods, particularly the EfficientNet-B4 architecture, which is well-known for its exceptional picture categorization performance. The model is trained using a large dataset of various photos of crops that have been impacted by pests. The main novelty is the model's precision in identifying and categorizing pests, which enables early and focused pest management techniques. This system operates far better than current ones, giving farmers a dependable instrument to quickly identify and take care of pest-related problems. By using less chemical pesticides, the initiative helps to increase crop output, reduce agricultural losses, and promote sustainable farming methods.