Grape leaf disease detection is a critical aspect of viticulture and plays an important role in ensuring the health and productivity of grapevines. The health of grape leaves is indicative of the overall well-being of the vineyard, as diseases can significantly impact grape quality and yield. The leaves of any plant stand first as an initial indicator for the health diagnosis of any plant. Grape leaf disease detection is a critical aspect of viticulture and plays a significant role in ensuring the health and productivity of grapevines. The health of grape leaves is indicative of the overall well-being of the plant, as diseases can significantly impact grape quality and yield. In this paper, we proposed and Cascaded Convolution Neural Network (C2N2) which trains the leaf features in a cascaded fashion. Thus, the network ensures high performance with global average pooling. The reason for using global average pooling is to avoid information loss during regularization. In the experimental setting on the Plan Village dataset especially on the leaf of grapes, the observation ensures satisfactory results with nearly 100% accuracy with very less number of epochs. The emergence of advanced technologies, such as machine learning, image analysis, and remote sensing, has revolutionized the process.