In the realm of agriculture, where crop health is integral to global food security, Our focus is on the early detection of crop diseases. Leveraging Convolutional Neural Networks (CNNs) on a diverse dataset of crop images, our study focuses on the development, training, and optimization of these networks to achieve accurate and timely disease classification. The first segment demonstrates the efficacy of CNN architecture and optimization strategy, showcasing the potential of deep learning models in automating the identification process. The synergy of robust disease detection and interpretability through Explainable Artificial Intelligence (XAI) presented in this work marks a significant stride toward bridging the gap between advanced technology and precision agriculture. By employing visualization, the research seeks to unravel the decision-making processes of our models. XAI Visualization method emerges as notably superior in terms of accuracy, hinting at its better identification of the disease, this method achieves an accuracy of 89.75\%, surpassing both the heat map model and the LIME explanation method. This not only enhances the transparency and trustworthiness of the predictions but also provides invaluable insights for end-users, allowing them to comprehend the diagnostic features considered by the complex algorithm.