T.S.R.Krishna Prasad,Kollati Ribka,J Venkata Guru Prasad,Jyothi Ayyappa Prasanna Kumar,Madugula Ajay Kumar 03 04 Plant diseases pose a significant threat to agricultural productivity and food security making early andaccurate detection essential Traditional image processing and machine learning techniques often struggle to handlevariations in lighting background noise and complex disease patterns To address these challenges this paperproposes a hybrid plant disease prediction system that integrates quantuminspired image processing with deepneural networks The proposed approach utilizes anglebased quantum encoding to transform leaf images intoquantumlike representations enabling efficient and discriminative feature extraction Statistical measurementsderived from these representations are combined with color and texture features to form a robust feature setThese features are then fed into a deep neural network model for multiclass disease classification The system isdesigned to be scalable and robust capable of handling diverse plant disease categories under varying environmentalconditions Experimental results demonstrate that the proposed method significantly improves classificationaccuracy and reliability compared to conventional approaches The developed framework can be effectively used inrealworld agricultural decision support systems for early disease diagnosis and crop management Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026