The disease prevalence among different plant species reduces the grain production, significantly affects the growth in terms of quantity and quality. Early diagnosis of the ailment effectively minimizes the plant devastation and improves the condition. Mostly, people involved in the agriculture-related activity identifies the abnormalities in the plants. However, in some cases, the chance of manual error is high during the diagnosis of the plant status. It further leads to economic deprivation, affects the livelihood of the agriculturalists. Effective monitoring and diagnosing plant conditions can be automated with available tools and applications. Such systems serve as virtual assistance to the needy anytime, which fills the gap of the presence of domain expertise. Moreover, automated screening tools contribute to elevated agricultural activities and ensures security upon food production and availability. These applications are powered by advanced, intelligent computational models for an accurate diagnosis. Deep learning algorithms are gaining more attention in the field of real-time monitoring and assistance due to its higher precision over complex applications. However, not all the models fit for applications with different environmental conditions. In this paper, a deep cluster-based plant disease categorization system is proposed to find the discriminative patterns from the images of different categories of maize crop. The proposed regularized deep clustering (RDC) algorithm combines the effectiveness of the convolutional autoencoder model alongside local structure preservation constraints and regularization. The proposed model is compared with state-of-the-art deep clustering algorithms to exhibit the efficacy of the RDC under different modes of clustering performance evaluation. This system acts as an effective tool for targeted users by providing them better assistance on plant disease diagnosis. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.