The deep learning models for crop diseases and pest classification research examined how deep learning might improve farming methods, particularly to accurately classify pests and diseases that affect crops. The importance of crop diseases and pests to world food security was highlighted in the introduction, along with the need for new approaches, such as deep learning models, to improve the accuracy and effectiveness of pest and disease control in farming. To evaluate the classification accuracy, the secondary datasets obtained from the Kaggle website were used to train and test various deep learning models, one of which was DenseNet. The researcher used a thorough assessment methodology to compare DenseNet's performance to that of other models, including AlexNet, EfficientNet, Visual Geometry Group, and Convolutional Neural Network. With an impressive accuracy score of 96.988% on the maize disease dataset and 96.9382% on the pest dataset, DenseNet proved to be the best model among the others. More accurate predictions were the result of DenseNet's capacity to effectively collect intricate characteristics and patterns within the visual data, which led to its improved performance. The researcher examined the implications of DenseNet's high accuracy in the discussion section, implying that its sophisticated design rendered it optimal for the categorization of agricultural diseases and pests. In addition, the researcher investigated the feasibility of incorporating DenseNet into practical agricultural systems, where its strong performance might greatly enhance methods of crop monitoring and disease control. The discussion came to a close with suggestions for future studies, such as looking at whether DenseNet can be used for other types of crops and if hybrid models or transfer learning may improve its performance.