Intelligent Segmentation-Driven Architecture for Automated Potato Plant Disease Classification and Severity Prediction with Convolutional Neural Optimization

Abstract

Potato crop production is significantly affected by fungal, bacterial, and environmental diseases that reduce yield quality and create economic challenges for agricultural systems. Accurate identification of disease type and severity level at an early stage is essential for implementing effective crop management strategies. Conventional visual inspection methods are often dependent on agricultural expertise, time-consuming, and vulnerable to subjective interpretation. This research presents an intelligent segmentation-driven architecture for automated potato plant disease classification and severity prediction using convolutional neural optimization. The proposed framework integrates image segmentation, feature refinement, and deep transfer learning principles to enhance disease localization, classification accuracy, and severity estimation. The architecture employs an optimized convolutional learning pipeline that focuses on extracting discriminative visual patterns from infected plant regions while minimizing background interference. The research framework is conceptually positioned around data-driven decision intelligence, where automated prediction mechanisms transform complex image observations into actionable agricultural insights. Similar decision-oriented artificial intelligence approaches demonstrate the importance of predictive models that utilize structured feature information for reliable classification outcomes (Krishnan et al., 2025). The proposed system contributes toward precision agriculture by providing a scalable approach for automated plant health monitoring, reducing dependency on manual diagnosis, and supporting sustainable crop management. The study also discusses architectural limitations, computational considerations, and future opportunities for integrating adaptive learning mechanisms into agricultural intelligence platforms.