Plant diseases represent a major threat to global food security, particularly impacting staple crops such as wheat, rice, and cotton. These diseases contribute to global yield losses estimated between 13 and 22%, resulting in billions of dollars in economic damage. Wheat accounts for approximately 10% of global agricultural output, rice is a key export commodity, and cotton is essential for fiber production—making these crops especially vulnerable to diseases like wheat rust, rice blast, and various cotton infections, which can severely disrupt both productivity and economic stability. To address this challenge, this chapter introduces a deep learning-based approach using a modified Xception architecture for plant disease detection. Unlike traditional convolutional neural networks (CNNs), which often struggle with efficiency and generalization, the proposed model demonstrates exceptional performance across all three crops. It achieved classification accuracies of 99.7% for wheat diseases, 100.0% for rice diseases, and 97.7% for cotton diseases, with an overall average accuracy of 98.6%. These results surpass those of widely used models such as ResNet-50 and Inception-v3. Moreover, with precision and recall rates exceeding 97% across all disease categories, the model shows strong potential for real-time disease diagnosis. This capability could significantly benefit farmers and support the advancement of precision agriculture.