Classifying agricultural pests is a crucial task in precision agriculture, which em- ploys technology to enhance farming techniques and increase crop productivity. Accurately identifying and categorizing pests is necessary for effective pest manage- ment because different pests require different control measures. This study conducts an exploration of Agricultural Pests Classification (APC), which will significantly benefit every Bangladeshi engaged in animal husbandry. The system can be practi- cally implemented to aid farmers in pest management, resulting in increased crop yield and reduced pesticide usage. The proposed approach in this paper utilizes Con- volutional Neural Network (CNN)-based pre-trained deep transfer learning classifiers (CNN-TLCs) to automatically categorize agricultural pests from the public Kaggle dataset “Agricultural Pests,” which includes 5534 raw images of 12 different varieties of agricultural pests. Multiple image processing techniques such as image cropping, resizing, rotation, color conversion, filtering, and contrast enhancement were applied to obtain high-quality images to train the models and achieve maximum accuracy. By utilizing Inception-V3 and DenseNet-201 models on the preprocessed images, the system obtained an accuracy of 97.22% and 97.51%, respectively. The high accuracy of the proposed method indicates its effectiveness in recognizing agricultural pests from digital images and helping farmers prevent pest infestations, leading to increased crop production and positively impacting the national economy.